Author: "Institute for Surgical Technology and Biomechanics [Bern] (ISTB)" - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Institute for Surgical Technology and Biomechanics [Bern] (ISTB)"' showing total 32 results

Start Over Author "Institute for Surgical Technology and Biomechanics [Bern] (ISTB)"

32 results on '"Institute for Surgical Technology and Biomechanics [Bern] (ISTB)"'

1. Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

Author: Thierry Brouard, Qianqian Tong, Mattias P. Heinrich, Guoyan Zheng, Jennifer Keegan, Pheng-Ann Heng, Jean-Yves Ramel, Darko Štern, Weixin Si, Guodong Zeng, Zenglin Shi, Chengjia Wang, David N. Firmin, Xin Yang, Chenchen Sun, Örjan Smedby, Martin Urschler, Lei Li, David E. Newby, Chunliang Wang, Ulas Bagci, Julien Oster, Aliasghar Mortazi, Christian Payer, Raad Mohiaddin, Kawal Rhode, Tom MacGillivray, Xiangyun Liao, Sebastien Ourselin, Gaetan Galisot, Guanyu Yang, Xiahai Zhuang, Guang Yang, Cheng Bian, British Heart Foundation, Computer science department [University College London] (UCL-CS), University College of London [London] (UCL), Shanghai Jiao Tong University [Shanghai], Graz University of Technology [Graz] (TU Graz), Ludwig Boltzmann Institute for Clinical Forensic Imaging [Graz] (LBI-CFI), Institute of Medical Informatics [Lübeck], Universität zu Lübeck [Lübeck], Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Royal Institute of Technology [Stockholm] (KTH ), Department of Biomedical Engineering and Health Systems [Stockholm], Shenzhen University [Shenzhen], Peking University [Beijing], The Chinese University of Hong Kong [Hong Kong], Center for Research in Computer Vision [Orlando] (CRCV), University of Central Florida [Orlando] (UCF), Nanjing Southeast University, Laboratoire d'Informatique Fondamentale et Appliquée de Tours (LIFAT), Centre National de la Recherche Scientifique (CNRS)-Université de Tours-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Reconnaissance des formes et analyse d'images (RFAI), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université de Tours-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), School of Computer Science [Wuhan], Wuhan University [China], Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences [Changchun Branch] (CAS), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Centre for Cardiovascular Science [Edinburgh] (BHF), University of Edinburgh, Division of Imaging Sciences, King‘s College London, Department of Imaging Royal Brompton Hospital, Royal Brompton Hospital, National Heart and Lung Institute [London] (NHLI), Imperial College London-Royal Brompton and Harefield NHS Foundation Trust, This work was funded in part by the National Natural Science Foundation of China (NSFC) grant (61971142), the Science and Technology Commission of Shanghai Municipality grant (17JC1401600) and the British Heart Foundation Project grant (PG/16/78/32402)., Université de Tours (UT)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université de Tours (UT)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL)
Subjects: FOS: Computer and information sciences, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Datasets as Topic, Health Informatics, Overfitting, Benchmark, Article, 09 Engineering, 030218 nuclear medicine & medical imaging, Set (abstract data type), 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Image Processing, Computer-Assisted, Humans, symbols.heraldic_charge, Radiology, Nuclear Medicine and imaging, Segmentation, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Challenge, Multi-modality, 11 Medical and Health Sciences, Radiological and Ultrasound Technology, business.industry, Deep learning, Whole Heart Segmentation, Heart shape, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Heart, Benchmarking, Magnetic Resonance Imaging, Computer Graphics and Computer-Aided Design, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Nuclear Medicine & Medical Imaging, Benchmark (computing), symbols, Computer Vision and Pattern Recognition, Artificial intelligence, Tomography, X-Ray Computed, business, Algorithm, Algorithms, 030217 neurology & neurosurgery, Test data
Abstract: Highlights • This work presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. • This work introduces the related information to the challenge, discusses the results from the conventional methods and deep learning-based algorithms, and provides insights to the future research. • The challenge provides a fair and intuitive comparison framework for methods developed and being developed for WHS. • The challenge provides the training datasets with manually delineated ground truths and evaluation for an ongoing development of MM-WHS algorithms., Graphical abstract This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MMWHS) challenge, in conjunction with MICCAI-STACOM 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. Several of them also reported poor results in the blinded evaluation, probably due to over fitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource., Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).
Published: 2019
Full Text: View/download PDF

2. Compressive behaviour of uniaxially aligned individual mineralised collagen fibres at the micro- and nanoscale

Author: Rainer J. Beck, Alexander Groetsch, Jakob Schwiedrzik, Philippe K. Zysset, Aurélien Gourrier, Jonathan D. Shephard, Michael Sztucki, Uwe Wolfram, Johann Michler, School of Engineering and Physical Sciences, Heriot-Watt University, Heriot-Watt University [Edinburgh] (HWU), Laboratoire Interdisciplinaire de Physique [Saint Martin d’Hères] (LIPhy), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Swiss Federal Laboratories for Materials Science and Technology [Thun] (EMPA), European Synchroton Radiation Facility [Grenoble] (ESRF), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), and University of Bern
Subjects: Compressive Strength, Turkey, [PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], 0206 medical engineering, Composite number, Biomedical Engineering, 02 engineering and technology, Matrix (biology), Fibril, Biochemistry, [SPI.MAT]Engineering Sciences [physics]/Materials, Biomaterials, X-Ray Diffraction, Phase (matter), Scattering, Small Angle, Composite material, [PHYS.MECA.BIOM]Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph], 610 Medicine & health, Molecular Biology, ComputingMilieux_MISCELLANEOUS, Small-angle X-ray scattering, General Medicine, 620 Engineering, 021001 nanoscience & nanotechnology, Compression (physics), 020601 biomedical engineering, Extracellular Matrix, Compressive strength, 570 Life sciences, biology, Collagen, Stress, Mechanical, Deformation (engineering), 0210 nano-technology, Biotechnology
Abstract: The increasing incidence of osteoporotic bone fractures makes fracture risk prediction an important clinical challenge. Computational models can be utilised to facilitate such analyses. However, they critically depend on bone’s underlying hierarchical material description. To understand bone’s irreversible behaviour at the micro- and nanoscale, we developed an in situ testing protocol that allows us to directly relate the experimental data to the mechanical behaviour of individual mineralised collagen fibres and its main constitutive phases, the mineralised collagen fibrils and the mineral nanocrystals, by combining micropillar compression of single fibres with small angle X-ray scattering (SAXS) and X-ray diffraction (XRD). Failure modes were assessed by SEM. Strain ratios in the elastic region at fibre, fibril and mineral levels were found to be approximately 22:5:2 with strain ratios at the point of compressive strength of 0.23 ± 0.11 for fibril-to-fibre and 0.07 ± 0.01 for mineral-to-fibre levels. Mineral-to-fibre levels showed highest strain ratios around the apparent yield point, fibril-to-fibre around apparent strength. The mineralised collagen fibrils showed a delayed mechanical response, contrary to the mineral phase, which points towards preceding deformations of mineral nanocrystals in the extrafibrillar matrix. No damage was measured at the level of the mineralised collagen fibre which indicates an incomplete separation of the mineral and collagen, and an extrafibrillar interface failure. The formation of kink bands and the gradual recruitment of fibrils upon compressive loading presumably led to localised strains. Our results from a well-controlled fibrillar architecture provide valuable input for micromechanical models and computational non-linear bone strength analyses that may provide further insights for personalised diagnosis and treatment as well as bio-inspired implants for patients with bone diseases. Statement of significance Musculoskeletal diseases such as osteoporosis, osteoarthritis or bone cancer significantly challenge health care systems and make fracture risk prediction and treatment optimisation important clinical goals. Computational methods such as finite element models have the potential to optimise analyses but highly depend on underlying material descriptions. We developed an in situ testing set-up to directly relate experimental data to the mechanical behaviour of bone’s fundamental building block, the individual mineralised collagen fibre and its main constituents. Low multilevel strain ratios suggest high deformations in the extrafibrillar matrix and energy dissipation at the interfaces, the absence of damage indicates both an incomplete separation between mineral and collagen and an extrafibrillar interface failure. The formation of kink bands in the fibril-reinforced composite presumably led to localised strains. The deformation behaviour of a well-controlled fibrillar architecture provides valuable input for non-linear bone strength analyses.
Published: 2019
Full Text: View/download PDF

3. Mechanical restoration and failure analyses of a hydrogel and scaffold composite strategy for annulus fibrosus repair

Author: Alexander Bürki, Sébastien Blanquer, Rose G. Long, James C. Iatridis, Philippe K. Zysset, Dirk W. Grijpma, David Eglin, Andrew C. Hecht, Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Laboratoire de Chimie de la Matière Condensée de Paris (site Paris VI) (LCMCP (site Paris VI)), Université Pierre et Marie Curie - Paris 6 (UPMC)-Collège de France (CdF (institution))-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), University of Twente, Nanotechnology and Biophysics in Medicine (NANOBIOMED), Faculty of Science and Technology, Biomaterials Science and Technology, and University of Twente [Netherlands]
Subjects: consultant Medtronic Spine, Scaffold, [SDV]Life Sciences [q-bio], Composite number, Fibrin Tissue Adhesive, 02 engineering and technology, Biochemistry, Intervertebral disk degeneration, 0302 clinical medicine, Implants, Experimental, editorial board of J. Spinal Disorders, 610 Medicine & health, Diskectomy, surgical advisory board Zimmer Spine, Orthopaedics Today JCI: Grant funding as listed annulus fibrosus repair, INTERVERTEBRAL DISC, Tissue Scaffolds, intervertebral disc degeneration, NEEDLE PUNCTURE, Biomechanics, DISKECTOMY, ANULAR REPAIR, Hydrogels, General Medicine, consultant and royalties from Zimmer Spine, DEGENERATION, musculoskeletal system, intervertebral disc herniation, Prosthesis Failure, medicine.anatomical_structure, Self-healing hydrogels, Spine Surgery Today, Intervertebral disk herniation, Annular closure, Biotechnology, medicine.medical_specialty, Materials science, LUMBAR SPINE, 0206 medical engineering, HERNIATION, Am J Orthopaedics, Biomedical Engineering, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, Article, Biomaterials, 03 medical and health sciences, medicine, Animals, Humans, [CHIM]Chemical Sciences, Molecular Biology, Intervertebral disc, Annulus fibrosus repair, NUCLEUS REPLACEMENT, Spine biomechanics, 020601 biomedical engineering, BIOMECHANICS, Surgery, Equipment Failure Analysis, MODEL, Intervertebral disk, [CHIM.POLY]Chemical Sciences/Polymers, 13. Climate action, 570 Life sciences, biology, Cattle, 030217 neurology & neurosurgery, Biomedical engineering
Abstract: Unrepaired defects in the annulus fibrosus of intervertebral disks are associated with degeneration and persistent back pain. A clinical need exists for a disk repair strategy that can seal annular defects, be easily delivered during surgical procedures, and restore biomechanics with low risk of herniation. Multiple annulus repair strategies were developed using poly(trimethylene carbonate) scaffolds optimized for cell delivery, polyurethane membranes designed to prevent herniation, and fibrin-genipin adhesive tuned to annulus fibrosus shear properties. This three-part study evaluated repair strategies for biomechanical restoration, herniation risk and failure mode in torsion, bending and compression at physiological and hyper-physiological loads using a bovine injury model. Fibrin-genipin hydrogel restored some torsional stiffness, bending ROM and disk height loss, with negligible herniation risk and failure was observed histologically at the fibrin-genipin mid-substance following rigorous loading. Scaffold-based repairs partially restored biomechanics, but had high herniation risk even when stabilized with sutured membranes and failure was observed histologically at the interface between scaffold and fibrin-genipin adhesive. Fibrin-genipin was the simplest annulus fibrosus repair solution evaluated that involved an easily deliverable adhesive that filled irregularly-shaped annular defects and partially restored disk biomechanics with low herniation risk, suggesting further evaluation for disk repair may be warranted.Statement of significanceLower back pain is the leading cause of global disability and commonly caused by defects and failure of intervertebral disk tissues resulting in herniation and compression of adjacent nerves. Annulus fibrosus repair materials and techniques have not been successful due to the challenging mechanical and chemical microenvironment and the needs to restore biomechanical behaviors and promote healing with negligible herniation risk while being delivered during surgical procedures. This work addressed this challenging biomaterial and clinical problem using novel materials including an adhesive hydrogel, a scaffold capable of cell delivery, and a membrane to prevent herniation. Composite repair strategies were evaluated and optimized in quantitative three-part study that rigorously evaluated disk repair and provided a framework for evaluating alternate repair techniques. (C) 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Published: 2016
Full Text: View/download PDF

4. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Author: Bakas, Spyridon, Reyes, Mauricio, Battistella, Enzo, Chandra, Siddhartha, Estienne, Théo, Fidon, Lucas, Vakalopoulou, Maria, Sun, Roger, Al., Et, Deutsch, Éric, Robert, Charlotte, Paragios, Nikos, Menze, Bjoern, Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec, Organ Modeling through Extraction, Representation and Understanding of Medical Image Content (GALEN-POST), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Département de radiothérapie [Gustave Roussy], and Institut Gustave Roussy (IGR)
Subjects: RANO, BraTS, Tumor, Radiomics, Survival, Progression, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV]Life Sciences [q-bio], Brain, Glioma, Segmentation, RECIST, Machine learning, [INFO]Computer Science [cs], [SDV.IB]Life Sciences [q-bio]/Bioengineering, Challenge, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Glioblastoma
Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.
Published: 2018

5. Why rankings of biomedical image analysis competitions should be interpreted with care

Author: Oskar Maier, Carolin Feldmann, Wiro J. Niessen, Bennett A. Landman, Michal Kozubek, Peter M. Full, Christian Stock, Patrick Scholz, Hrvoje Bogunovic, Katrin Honauer, Keno März, Marko Stankovic, Korsuk Sirinukunwattana, Ching-Wei Wang, Henning Müller, Nasir M. Rajpoot, Aaron Carass, Sinan Onogur, Alejandro F. Frangi, Stefanie Speidel, Peter Neher, Bram van Ginneken, Matthias Eisenmann, Pierre Jannin, Bjoern H. Menze, Allan Hanbury, Lena Maier-Hein, Annika Reinke, Guoyan Zheng, Gregory C. Sharp, Danail Stoyanov, Marc-André Weber, Andrew P. Bradley, Abdel Aziz Taha, Tal Arbel, Fons van der Sommen, Klaus H. Maier-Hein, Annette Kopp-Schneider, Jonchère, Laurent, German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), McGill University = Université McGill [Montréal, Canada], Medizinische Universität Wien = Medical University of Vienna, Queensland University of Technology [Brisbane] (QUT), Department of Computer Science [Baltimore], Johns Hopkins University (JHU), University of Leeds, Radboud University Medical Center [Nijmegen], Vienna University of Technology (TU Wien), Heidelberg University, Masaryk University [Brno] (MUNI), Vanderbilt University [Nashville], Institute of Medical Informatics [Lübeck], Universität zu Lübeck = University of Lübeck [Lübeck], Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Haute Ecole Spécialisée de Suisse Occidentale (HES-SO), Departments of Radiology, Nuclear Medicine and Medical Informatics, Erasmus University Medical Center [Rotterdam] (Erasmus MC), University of Warwick [Coventry], Massachusetts General Hospital [Boston], University of Oxford, National Center for Tumor Diseases [Dresden] (NCT), Technische Universität Dresden = Dresden University of Technology (TU Dresden)-German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ)-Helmholtz-Zentrum Dresden-Rossendorf (HZDR), University College of London [London] (UCL), Research Studios Austria, Eindhoven University of Technology [Eindhoven] (TU/e), National Taiwan University of Science and Technology, University Medical Center Rostock, Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Universität zu Lübeck [Lübeck], University of Oxford [Oxford], Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), Medical Informatics, Radiology & Nuclear Medicine, and Video Coding & Architectures
Subjects: FOS: Computer and information sciences, Biomedical Research, Technology Assessment, Biomedical, Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Image Processing, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, General Physics and Astronomy, Biomedical Research/methods, Diagnostic Imaging/classification, 030218 nuclear medicine & medical imaging, 0302 clinical medicine, Technology Assessment, Surveys and Questionnaires, Image Processing, Computer-Assisted, Computer-Assisted/methods, lcsh:Science, computer.programming_language, media_common, Multidisciplinary, Rank (computer programming), [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Biomedical/methods, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Rare cancers Radboud Institute for Health Sciences [Radboudumc 9], Diagnostic Imaging, Scheme (programming language), Image Processing, Computer-Assisted/methods, Technology Assessment, Biomedical/methods, media_common.quotation_subject, Science, Biomedical Technology, Biomedical Technology/classification, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, All institutes and research themes of the Radboud University Medical Center, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Fraction (mathematics), Quality (business), Author Correction, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Reproducibility of Results, General Chemistry, Data science, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Ranking, lcsh:Q, computer, 030217 neurology & neurosurgery, RC, Test data
Abstract: International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future., Comment: Article published in Nature Communications: https://rdcu.be/bRmNr
Published: 2018
Full Text: View/download PDF

6. MICROCRACK EVOLUTION IN MICROINDENTATION OF OVINE CORTCAL BONE INVESTIGATED WITH TIME LAPSED SRµCT

Author: Wolfram, Uwe, Schwiedrzik, Jakob, Bürki, Alexander, Rack, Alexander, Olivier, Cécile, Peyrin, Françoise, James, Best, Michler, Johann, Zysset, Philippe, School of Engineering Physical Sciences (SEPS), Heriot-Watt University [Edinburgh] (HWU), Swiss Federal Laboratories for Materials Science and Technology [Thun] (EMPA), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, European Synchrotron Radiation Facility (ESRF), Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratory for Mechanics of Materials and Nanostructures [Thun] (EMPA), Rayet, Béatrice, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [PHYS.MECA.BIOM] Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph], [PHYS.MECA.BIOM]Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph]
Abstract: International audience; Skeletal diseases such as osteoporosis constitute a seri-ous socio-economic burden in aging societies. Patient specific tissue strength estimation could be helpful for personalised treatment strategies. Reference point in-dentation (RPI) [1] that derives from instrumented in-dentation [2] has been proposed. With imprints of sev-eral 100 μm at indentation depths of ~80 μm several structural units are affected. It is necessary to better un-derstand the processes under the indenter tip but studies investigating the impact of larger indenters are currently missing. Combining high resolution imaging with in situ microindentation could help to investigate the zone un-derneath the indenter and observe emerging cracks. We aimed at (i) combining microindentations with synchro-tron radiation micro-computed tomography (SRμCT) to investigate (ii) crack systems under two different indent-ers (iii) in axial or transverse testing direction.
Published: 2017

7. Characterizing microcrack orientation distribution functions in osteonal bone samples

Author: Wolfram, U., Schwiedrzik, J., Mirzaali, M., Bürki, A., Varga, P., Olivier, Cécile, Peyrin, F., Zysset, P., Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, School of Engineering and Physical Science, Institute for Mechanical, Process and Energy Engineering, Heriot-Watt University [Edinburgh] (HWU), Swiss Federal Laboratories for Materials Science and Technology [Thun] (EMPA), AO Research institute Davos (ARI), AO Foundation, European Synchrotron Radiation Facility (ESRF), Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Microcrack segmentation, Synchrotron radiation, X-ray phase micro-tomography, Microdamage, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Cortical bone, 570 Life sciences, biology, Orientation distribution functions, 610 Medicine & health, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; Prefailure microdamage in bone tissue is considered to be the most detrimental factor in defining its strength and toughness with respect to age and disease. To understand the influence of microcracks on bone mechanics it is necessary to assess their morphology and three-dimensional distribution. This requirement reaches beyond classic histology and stereology, and methods to obtain such information are currently missing. Therefore, the aim of the study was to develop a methodology that allows to characterize three-dimensional microcrack distributions in bulk bone samples.Four dumbbell-shaped specimens of human cortical bone of a 77-year-old female donor were loaded beyond yield in either tension, compression or torsion (one control). Subsequently, synchrotron radiation micro-computed tomography (SRμCT) was used to obtain phase-contrast images of the damaged samples. A microcrack segmentation algorithm was developed and used to segment microcrack families for which microcrack orientation distribution functions were determined.Distinct microcrack families were observed for each load case that resulted in distinct orientation distribution functions. Microcracks had median areas of approximately 4.7 inline imagem2, 33.3 inline imagem2 and 64.0 inline imagem2 for tension, compression and torsion. Verifying the segmentation algorithm against a manually segmented ground truth showed good results when comparing the microcrack orientation distribution functions. A size dependence was noted when investigating the orientation distribution functions with respect to the size of the volume of interest used for their determination. Furthermore, a scale separation between tensile, compressive and torsional microcracks was noticeable. Visual comparison to classic histology indicated that microcrack families were successfully distinguished.We propose a methodology to analyse three-dimensional microcrack distributions in overloaded cortical bone. Such information could improve our understanding of bone microdamage and its impact on bone failure in relation to tissue age and disease.Lay descriptionPrefailure microdamage in bone tissue is considered to be the most detrimental factor in defining its strength and toughness with respect to age and disease. To understand the influence of microcracks on bone mechanics it is necessary to assess their morphology and three-dimensional distribution. This requirement reaches beyond classic histology and stereology, and methods to obtain such information are currently missing.
Published: 2016
Full Text: View/download PDF

8. The JEM-EUSO mission : an introduction

Author: J. Karczmarczyk, A. Haungs, T. Murakami, Tomás Belenguer, Piero Vallania, M. D. Sabau, M. Serra, J. Bayer, C. de la Taille, K. Katahira, Luis A. Anchordoqui, T. Batsch, D. Supanitsky, Hanna Rothkaehl, R. Nava, N. Sakaki, Y. Karadzhov, I. Kaneko, K. Mase, J. Watts, L. Valore, Rossella Caruso, H. J. Crawford, C. Pennypacker, I. Rodríguez, Shoichi Ogio, Patrick J. Reardon, Toshiki Tajima, G. Siemieniec-Oziȩbło, G. Masciantonio, Hitoshi Ohmori, Yoshiya Kawasaki, D. Allard, G. Castellini, M. E. Bertaina, Ken'ichi Nomoto, Silvia Ferrarese, Toshiyuki Nonaka, P. Prat, R. Young, M. Sakata, A. Guzmán, I. V. Yashin, T. Shirahama, T. Shibata, Alberto Cellino, P. Picozza, J. Lee, I. Fernández-Gómez, P. Baragatti, L. Marcelli, Ralph Engel, M. Karus, G. Sáez Cano, M. Nagata, A. Ebersoldt, L. Villaseñor, A. Bruno, Jeong-Sook Kim, L. R. Wiencke, J. Błȩcki, Karl Mannheim, D. Ikeda, D. Monnier-Ragaigne, A. Segreto, F. Tajima, Lech Wiktor Piotrowski, M. Rybczyński, Michael S. Briggs, A. J. de Castro, F. Sarazin, Domenico Finco, H. Lim, Frederic Trillaud, P. Gorodetzky, Dmitry V. Naumov, R. Bechini, Marc Weber, Katsuaki Asano, G. Cordero, M. C. Maccarone, Piotr Orleanski, N. Blanc, S. Piraino, J.N. Capdevielle, T. Patzak, S. Yoshida, J. Szabelski, Francesco Fenu, Yoshiki Tsunesada, Hiroyuki Sagawa, M. Wille, Andrii Neronov, Y. Uchihori, R. Matev, G. Giraudo, M. Suzuki, Satoshi Wada, H. Sato, C. De Donato, Markus Roth, J. N. Albert, T. Ogawa, Hajime Takami, B. A. Khrenov, G. Distratis, Fausto Guarino, O. Martinez, Masaki Fukushima, Thomas Schanz, Soon-Wook Kim, Takuji Nakamura, Jianyi Yang, Yoshitaka Itow, Shigeto Watanabe, H. Miyamoto, D. Maravilla, P. von Ballmoos, M. Ricci, K. Shinozaki, Angela V. Olinto, Fernando López, Andrea Santangelo, S. Selmane, Livio Conti, J. Fujimoto, A. Anzalone, Barbara Szabelska, T. Peter, David B. Cline, A. Zuccaro Marchi, Maxim Gonchar, Hirohiko M. Shimizu, M. D. Rodríguez Frías, F. Cafagna, G. Modestino, E. Joven, D. Campana, Francesco Isgrò, F. Dulucq, Z. Plebaniak, J. Blümer, Valentina Scotti, B. Keilhauer, Graciela B. Gelmini, M. Flamini, P. L. Biermann, Jörn Wilms, Javier Licandro, M. I. Panasyuk, J. F. Valdés-Galicia, L. G. Tkachev, O. A. Saprykin, Michiyuki Chikawa, B. Mot, Y. Hachisu, Inkyu Park, Frederic Jean Ronga, M. Dupieux, S. Csorna, T. Napolitano, S. Perez Cano, Y. Takizawa, L. Santiago Crúz, M. Putis, Humberto Ibarguen Salazar, Y. Arai, G. Vankova, I. Rusinov, A. Sobey, Y. Miyazaki, S. Blin-Bondil, C. De Santis, H. Tokuno, Y. Yamamoto, A. Insolia, M. Kleifges, Yukihiro Takahashi, B. Pastirčák, E. G. Judd, T. Pierog, M. Ave Pernas, Katsuhiko Tsuno, H. Prieto, R. Cremonini, S. Biktemerova, J. Watanabe, Pierre Barrillon, Mark Christl, O. Tibolla, Osvaldo Catalano, Gali Garipov, K. Kawai, I. Kreykenbohm, Sergei A. Sharakin, R. Tsenov, Vladimir Andreev, Marcos Reyes, Marco Casolino, Claudio Fornaro, K. Belov, C. Catalano, A. Pollini, J. Hernández Carretero, Pavel Klimov, M. Unger, C. Lachaud, S. Dagoret-Campagne, M. Di Martino, A. Marini, Toshikazu Ebisuzaki, T. J. Weiler, G. Osteria, S. Sánchez, S. Ahmad, Yoshimasa Kurihara, T. Tymieniecka, J. Sledd, H. Schieler, Mitsuteru Sato, C. González Alvarado, Alfonso Monaco, C. Blaksley, N. Tone, Ke Fang, M. Nagano, Taka Tomida, Daisuke Yonetoku, Roberto Bellotti, Andreas A. Berlind, M. Sanz Palomino, Tokonatsu Yamamoto, James H. Adams, M. Yu. Zotov, D. Kolev, Valerie Connaughton, H. H. Silva Lopez, Susana Briz, S. Falk, H. Ikeda, V. S. Morozenko, Hajime Yano, A. Dell’Oro, Toshitaka Kajino, K. Higashide, C. Moretto, P. Galeotti, C. Tenzer, T. Sugiyama, Tadeusz Wibig, K. Kudela, G. Roudil, B. Harlov, Carlo Vigorito, N. Tajima, M. Takeda, Shigehiro Nagataki, Guillaume Prévôt, M. Bogomilov, F. Kajino, H. W. Park, Massimiliano Bonamente, E. Kuznetsov, Alexander Kusenko, K. Słomińska, Akinori Saito, Ovidiu Vaduvescu, M. Lacombe, Claudio Cassardo, Dmitri Semikoz, Sug-Whan Kim, T. Mernik, Z. Włodarczyk, Simona Toscano, N. Inoue, L. del Peral, A. Franceschi, E. Parizot, Pavol Bobik, J. A. Morales de los Ríos, Susumu Inoue, A. Jung, T. Paul, K. Yoshida, G. Medina-Tanco, J. Geary, N. De Simone, Yoshihiko Mizumoto, Organisation de Micro-Électronique Générale Avancée (OMEGA), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de l'Accélérateur Linéaire (LAL), Université Paris-Sud - Paris 11 (UP11)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), AstroParticule et Cosmologie (APC (UMR_7164)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Laboratori Nazionali del Sud (LNS), Istituto Nazionale di Fisica Nucleare (INFN), Division of Biology, Kansas State University, Kansas State University, CTC, Università degli studi di Bari Aldo Moro = University of Bari Aldo Moro (UNIBA), Institut de recherche en astrophysique et planétologie (IRAP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), INAF - Osservatorio Astrofisico di Torino (OATo), Istituto Nazionale di Astrofisica (INAF), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Universities Space Research Association (USRA), Regione Emilia Romagna, Servizio Tecnico Bacini Enza e sinistra Secchia, Institute for Data Processing and Electronics (IPE), Karlsruhe Institute of Technology (KIT), Institute for Ecology, Universität Greifswald - University of Greifswald, Tokushima research center, Department of Physics and Astronomy [UCLA, Los Angeles], University of California [Los Angeles] (UCLA), University of California (UC)-University of California (UC), Istituto Nazionale di Fisica Nucleare, Sezione di Torino (INFN, Sezione di Torino), Institute of Applied Biotechnology and Basic Sciences, Rzeszow University, Department of Mechanical System (Hiroshima University), Hiroshima University, Nagoya University, Department of Pathology, Ludwig-Maximilians-Universität München (LMU), Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Institute of Experimental Physics of Kosice, Slovak Academy of Sciences (SAS), KEK (High energy accelerator research organization), iMagX/MIRO, Université Catholique de Louvain = Catholic University of Louvain (UCL), Instituto de Astrofisica de Canarias (IAC), The Open University of Japan [Chiba] (OUJ), Tohoku University [Sendai], Department of Earth and Planetary Science [Tokyo], Graduate School of Science [Tokyo], The University of Tokyo (UTokyo)-The University of Tokyo (UTokyo), Department of Electrical Engineering and Computer Sciences, University of Hyogo, INTEGRAL Science Data Center (ISDC), Geneva Observatory, Université de Genève = University of Geneva (UNIGE)-Université de Genève = University of Geneva (UNIGE), Service de dermatologie et vénéréologie (CHUV Lausanne), Centre Hospitalier Universitaire Vaudois [Lausanne] (CHUV), Laboratoire de Physique Théorique et Modèles Statistiques (LPTMS), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Centre IRMf de Marseille, Université de la Méditerranée - Aix-Marseille 2, Space Research Centre of Polish Academy of Sciences (CBK), Polska Akademia Nauk = Polish Academy of Sciences (PAN), Centre d'étude spatiale des rayonnements (CESR), Center for Mathematics and Physics, University of Aizu, Centre de recherche sur la Paléobiodiversité et les Paléoenvironnements (CR2P), Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Colorado School of Mines, Department of Physics (Department of Physics), Tokyo Metropolitan University [Tokyo] (TMU), Department of Radiation-Oncology, Università degli Studi di Firenze = University of Florence (UniFI), Graduate School of Materials Science, Nara Institute os Science and Technology, Observatoire océanologique de Banyuls (OOB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Max-Planck-Institut für Kernphysik (MPIK), Max-Planck-Gesellschaft, Vrije Universiteit Brussel (VUB), Laboratoire de Génie Electrique de Grenoble (G2ELab), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS), Institute of Sensor and Actuator Systems, Vienna University of Technology (TU Wien), Institut de Mécanique Céleste et de Calcul des Ephémérides (IMCCE), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Luxembourg Institute of Science and Technology (LIST), Dr. Karl-Remeis-Sternwarte, Erlangen Centre for Astroparticle Physics (ECAP), Friedrich-Alexander Universität Erlangen-Nürnberg (FAU)-Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Kyoto University, University of Connecticut (UCONN), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-École polytechnique (X), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Observatoire de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Université Paris Diderot - Paris 7 (UPD7), National Institute for Nuclear Physics (INFN), University of Bari and INFN section of Bari, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology in Zürich [Zürich] (ETH Zürich), University of California-University of California, Université Catholique de Louvain (UCL), The University of Tokyo, University of Geneva [Switzerland]-University of Geneva [Switzerland], Polska Akademia Nauk (PAN), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS), Tokyo Metropolitan University [Tokyo], University of Florence (UNIFI), High Energy Accelerator Research Organization (KEK), University of Tsukuba, Vrije Universiteit [Brussels] (VUB), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Technical University of Vienna [Vienna] (TU WIEN), PSL Research University (PSL)-PSL Research University (PSL)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Kyoto University [Kyoto], Adams, J. H., Ahmad, S., Albert, J. N., Allard, D., Anchordoqui, L., Andreev, V., Anzalone, A., Arai, Y., Asano, K., Ave Pernas, M., Baragatti, P., Barrillon, P., Batsch, T., Bayer, J., Bechini, R., Belenguer, T., Bellotti, R., Belov, K., Berlind, A. A., Bertaina, M., Biermann, P. L., Biktemerova, S., Blaksley, C., Blanc, N., Błȩcki, J., Blin Bondil, S., Blã¼mer, J., Bobik, P., Bogomilov, M., Bonamente, M., Briggs, M. S., Briz, S., Bruno, A., Cafagna, F., Campana, D., Capdevielle, J. N., Caruso, R., Casolino, M., Cassardo, C., Castellini, G., Catalano, C., Catalano, O., Cellino, A., Chikawa, M., Christl, M. J., Cline, D., Connaughton, V., Conti, L., Cordero, G., Crawford, H. J., Cremonini, R., Csorna, S., Dagoret Campagne, S., de Castro, A. J., De Donato, C., de la Taille, C., De Santis, C., del Peral, L., Dell’Oro, A., De Simone, N., Di Martino, M., Distratis, G., Dulucq, F., Dupieux, M., Ebersoldt, A., Ebisuzaki, T., Engel, R., Falk, S., Fang, K., Fenu, F., FernÃ¡ndez GÃ³mez, I., Ferrarese, S., Finco, D., Flamini, M., Fornaro, C., Franceschi, A., Fujimoto, J., Fukushima, M., Galeotti, P., Garipov, G., Geary, J., Gelmini, G., Giraudo, G., Gonchar, M., GonzÃ¡lez Alvarado, C., Gorodetzky, P., Guarino, Fausto, Guzmã¡n, A., Hachisu, Y., Harlov, B., Haungs, A., HernÃ¡ndez Carretero, J., Higashide, K., Ikeda, D., Ikeda, H., Inoue, N., Inoue, S., Insolia, A., Isgro', Francesco, Itow, Y., Joven, E., Judd, E. G., Jung, A., Kajino, F., Kajino, T., Kaneko, I., Karadzhov, Y., Karczmarczyk, J., Karus, M., Katahira, K., Kawai, K., Kawasaki, Y., Keilhauer, B., Khrenov, B. A., Kim, Jeong Sook, Kim, Soon Wook, Kim, Sug Whan, Kleifges, M., Klimov, P. A., Kolev, D., Kreykenbohm, I., Kudela, K., Kurihara, Y., Kusenko, A., Kuznetsov, E., Lacombe, M., Lachaud, C., Lee, J., Licandro, J., Lim, H., Lã³pez, F., Maccarone, M. C., Mannheim, K., Maravilla, D., Marcelli, L., Marini, A., Martinez, O., Masciantonio, G., Mase, K., Matev, R., Medina Tanco, G., Mernik, T., Miyamoto, H., Miyazaki, Y., Mizumoto, Y., Modestino, G., Monaco, A., Monnier Ragaigne, D., Morales de los RÃos, J. A., Moretto, C., Morozenko, V. S., Mot, B., Murakami, T., Nagano, M., Nagata, M., Nagataki, S., Nakamura, T., Napolitano, T., Naumov, D., Nava, R., Neronov, A., Nomoto, K., Nonaka, T., Ogawa, T., Ogio, S., Ohmori, H., Olinto, A. V., Orleański, P., Osteria, G., Panasyuk, M. I., Parizot, E., Park, I. H., Park, H. W., Pastircak, B., Patzak, T., Paul, T., Pennypacker, C., Perez Cano, S., Peter, T., Picozza, P., Pierog, T., Piotrowski, L. W., Piraino, S., Plebaniak, Z., Pollini, A., Prat, P., Prã©vã´t, G., Prieto, H., Putis, M., Reardon, P., Reyes, M., Ricci, M., Rodrãguez, I., Frãas, M. D. RodrÃguez, Ronga, F., Roth, M., Rothkaehl, H., Roudil, G., Rusinov, I., Rybczyński, M., Sabau, M. D., SÃ¡ez Cano, G., Sagawa, H., Saito, A., Sakaki, N., Sakata, M., Salazar, H., Sã¡nchez, S., Santangelo, A, Santiago CrÃºz, L., Sanz Palomino, M., Saprykin, O., Sarazin, F., Sato, H., Sato, M., Schanz, T., Schieler, H., Scotti, V., Segreto, A., Selmane, S., Semikoz, D., Serra, M., Sharakin, S., Shibata, T., Shimizu, H. M., Shinozaki, K., Shirahama, T., Siemieniec Oziȩbło, G., Silva LÃ³pez, H. H., Sledd, J., Słomińska, K., Sobey, A., Sugiyama, T., Supanitsky, D., Suzuki, M., Szabelska, B., Szabelski, J., Tajima, F., Tajima, N., Tajima, T., Takahashi, Y., Takami, H., Takeda, M., Takizawa, Y., Tenzer, C., Tibolla, O., Tkachev, L., Tokuno, H., Tomida, T., Tone, N., Toscano, S., Trillaud, F., Tsenov, R., Tsunesada, Y., Tsuno, K., Tymieniecka, T., Uchihori, Y., Unger, M., Vaduvescu, O., ValdÃ©s Galicia, J. F., Vallania, P., Valore, Laura, Vankova, G., Vigorito, C., Villaseã±or, L., von Ballmoos, P., Wada, S., Watanabe, J., Watanabe, S., Watts, J., Weber, M., Weiler, T. J., Wibig, T., Wiencke, L., Wille, M., Wilms, J., Włodarczyk, Z., Yamamoto, T., Yamamoto, Y., Yang, J., Yano, H., Yashin, I. V., Yonetoku, D., Yoshida, K., Yoshida, S., Young, R., Zotov, M. Y. u., Zuccaro Marchi, A., École polytechnique (X)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Sud - Paris 11 (UP11), Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Università degli studi di Bari Aldo Moro (UNIBA), Institut national des sciences de l'Univers (INSU - CNRS)-Université Toulouse III - Paul Sabatier (UT3), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), The University of Tokyo (UTokyo), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11), Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology, Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Observatoire de Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)
Subjects: Photon, Aperture, Ciencias Físicas, media_common.quotation_subject, Astrophysics::High Energy Astrophysical Phenomena, Cosmic ray, ultra-high energy cosmic rays, Astrophysics, law.invention, Telescope, Atmosphere, law, International Space Station, Neutrino, Ultra-high energy cosmic rays, Neutrinos, Neutrinos, Ultra-high energy cosmic ray, ComputingMilieux_MISCELLANEOUS, media_common, Physics, [PHYS]Physics [physics], Astrophysics::Instrumentation and Methods for Astrophysics, Astronomy, neutrinos, Astronomy and Astrophysics, Ultra-high energy cosmic rays, Astronomy and Astrophysic, Universe, Astronomía, Space and Planetary Science, Physics::Space Physics, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], CIENCIAS NATURALES Y EXACTAS
Abstract: 注記: The JEM-EUSO Collaboration 288名 [所属. 宇宙航空研究開発機構宇宙科学研究所 (JAXA)(ISAS) 池田, 博一; 鈴木, 睦; 矢野, 創], Accepted: 2015-10-19, Note: The JEM-EUSO Collaboration 288members [Affiliation. Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA)(ISAS) Ikeda, Hirokazu; Suzuki, Makoto; Yano, Hajime], 資料番号: SA1150231000
Published: 2015
Full Text: View/download PDF

9. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Author: Nicholas Ayache, Danial Lashkari, Yuliya Burren, Polina Golland, Duygu Sarikaya, Levente Lanczi, Florence Forbes, Binsheng Zhao, Michel Dojat, Brian B. Avants, Marcel Prastawa, Liang Zhao, Andac Hamamci, Jamie Shotton, Ben Glocker, Thomas J. Taylor, Joana Festa, Nicolas Cordier, Hervé Delingette, Johannes Slotboom, Doina Precup, Antonio Criminisi, Ezequiel Geremia, Lawrence H. Schwartz, Stephen J. Price, Gábor Székely, Carlos A. Silva, José Mariz, Owen M. Thomas, Tilak Das, Ender Konukoglu, Dong Hye Ye, Khan M. Iftekharuddin, Syed M. S. Reza, Xiaotao Guo, Koen Van Leemput, Raphael Meier, Senan Doyle, Christopher R. Durst, D. Louis Collins, Raj Jena, Tal Arbel, Elizabeth R. Gerstner, Darko Zikic, Çağatay Demiralp, Marc-André Weber, Keyvan Farahani, Nagesh K. Subbanna, Stefan Bauer, Nicholas J. Tustison, Sérgio Pereira, Bjoern H. Menze, Patricia Buendia, Jason J. Corso, Mauricio Reyes, Nuno Sousa, Max Wintermark, Jayashree Kalpathy-Cramer, Flor Vasseur, Justin Kirby, Gozde Unal, Andras Jakab, Roland Wiest, Tammy Riklin Raviv, Nigel M. John, N Porz, Michael Ryan, Hoo-Chang Shin, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Computational Image Analysis and Radiology (CIR lab), Medizinische Universität Wien = Medical University of Vienna, Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Massachusetts General Hospital [Boston], Department of Health and Human Services, National Institutes of Health [Bethesda] (NIH), Inselspital Bern, University of Debrecen Egyetem [Debrecen], Division of Medical Physics in Radiology [Heidelberg], German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Diagnostic and Interventional Radiology [Heidelberg], Heidelberg University Hospital [Heidelberg], Centre for Intelligent Machines (CIM), McGill University = Université McGill [Montréal, Canada], Penn Image Computing & Science Lab [Philadelphia] (PICSL), University of Pennsylvania, INFOTECH Soft, McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], State University of New York (SUNY), Microsoft Research [Cambridge] (Microsoft), Microsoft Research, Cambridge University Hospitals - NHS (CUH), University of Cambridge [UK] (CAM), Computer Science Department [Stanford], Stanford University, Department of radiology and medical imaging [Charlottesville], University of Virginia, Grenoble Institut des Neurosciences (GIN), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Universidade do Minho = University of Minho [Braga], Computer Science and Artificial Intelligence Laboratory [Cambridge] (CSAIL), Massachusetts Institute of Technology (MIT), Columbia University [New York], Faculty of Engineering and Natural Sciences (Sabanci University), Sabanci University [Istanbul], Old Dominion University [Norfolk] (ODU), Addenbrooke's Hospital, Cambridge University NHS Trust, Bangor University, Department of Surgery, Université de Genève = University of Geneva (UNIGE), Bristol Glaciology Centre, School of Geographical Sciences, Molecular Carcinogenesis [Sutton], Institute of cancer research, Center for Neuroscience and Cell Biology (CNC) (CNC), University of Coimbra [Portugal] (UC)-Neuroscience Research Domain, Computer Vision Laboratory - ETHZ [Zurich], Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Department of Radiology [San Francisco], University of California [San Francisco] (UC San Francisco), University of California (UC)-University of California (UC), Purdue University [West Lafayette], Department of Computer Science [New York], Scientific Computing and Imaging Institute (SCI Institute), University of Utah, Department of radiology (Massachusetts General Hospital), European Project: 291080,EC:FP7:ERC,ERC-2011-ADG_20110209,MEDYMA(2012), Menze, Bjoern, Biophysical Modeling and Analysis of Dynamic Medical Images. - MEDYMA - - EC:FP7:ERC2012-04-01 - 2017-03-31 - 291080 - VALID, [et al.], University of Pennsylvania [Philadelphia], University of Virginia [Charlottesville], Universidade do Minho, University of Geneva [Switzerland], University of California [San Francisco] (UCSF), University of California-University of California, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Golland, Polina, and Lashkari, Danial
Subjects: Computer science, Oncology/tumor, Brain tumor, [SCCO.COMP]Cognitive science/Computer science, Neuroimaging, Benchmark, computer.software_genre, Article, Set (abstract data type), [SCCO.COMP] Cognitive science/Computer science, Cut, medicine, Medical imaging, Humans, Electrical and Electronic Engineering, Image segmentation, Science & Technology, Radiological and Ultrasound Technology, business.industry, Brain, Pattern recognition, Glioma, medicine.disease, Magnetic Resonance Imaging, 3. Good health, Computer Science Applications, Benchmarking, Benchmark (computing), Data mining, Artificial intelligence, business, computer, Algorithms, Software, MRI
Abstract: In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients - manually annotated by up to four raters - and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource., National Center for Research Resources (U.S.) (P41-RR14075), National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01EB013565), National Institute of Biomedical Imaging and Bioengineering (U.S.) (U54-EB005149), National Institutes of Health (U.S.) (P41-RR13218), National Institute of Biomedical Imaging and Bioengineering (U.S.) (P41-EB-015902), National Cancer Institute (U.S.) (R15CA115464)
Published: 2014
Full Text: View/download PDF

10. Proceedings of the MICCAI Challenge on Multimodal Brain Tumor Image Segmentation (BRATS) 2013

Author: Menze, Bjoern, Reyes, Mauricio, Jakab, Andras, Gerstner, Elisabeth, Kirby, Justin, Farahani, Keyvan, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Computational Image Analysis and Radiology (CIR lab), Medizinische Universität Wien = Medical University of Vienna, Massachusetts General Hospital [Boston], Department of Health and Human Services, National Institutes of Health [Bethesda] (NIH), and Bjoern Menze and Mauricio Reyes and Andras Jakab and Elisabeth Gerstner and Justin Kirby and Keyvan Farahani
Subjects: [SCCO.COMP]Cognitive science/Computer science
Abstract: International audience; Because of their unpredictable appearance and shape, segmenting brain tumors from multi-modal imaging data is one of the most challenging tasks in medical image analysis. Although many different segmentation strategies have been proposed in the literature, it is hard to compare existing methods because the validation datasets that are used differ widely in terms of input data (structural MR contrasts; perfusion or diffusion data; ...), the type of lesion (primary or secondary tumors; solid or infiltratively growing), and the state of the disease (pre- or post-treatment). In order to gauge the current state-of-the-art in automated brain tumor segmentation and compare between different methods, we are organizing a Multimodal Brain Tumor Image Segmentation (BRATS) challenge that is held in conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013) on September 22nd, 2013 in Nagoya, Japan.
Published: 2013

11. Regional Analysis of Left Ventricle Function using a Cardiac-Specific Polyaffine Motion Model

Author: Maxime Sermesant, Nicolas Toussaint, Xavier Pennec, Kristin McLeod, Christof Seiler, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Division of Imaging Sciences, King‘s College London, and Sébastien Ourselin and Daniel Rueckert and Nicolas Smith
Subjects: Cardiac function curve, Cardiac cycle, Left bundle branch block, Dynamics (mechanics), 02 engineering and technology, Anatomy, medicine.disease, Motion (physics), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Ventricle, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 0202 electrical engineering, electronic engineering, information engineering, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 020201 artificial intelligence & image processing, Affine transformation, Pathological, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Mathematics
Abstract: Given the complex dynamics of cardiac motion, understanding the motion for both normal and pathological cases can aid in understanding how different pathological conditions effect, and are affected by cardiac motion. Naturally, different regions of the left ventricle of the heart move in different ways depending on the location, with significantly different dynamics between the septal and free wall, and basal and apical regions. Therefore, studying the motion at a regional level can provide further information towards identifying abnormal regions for example. The 4d left ventricular motion of a given case was characterised by a low number of parameters at a region level using a cardiac specific polyaffine motion model. The motion was then studied at a regional level by analysing the computed affine transformation matrix of each region. This was used to examine the regional evolution of normal and pathological subjects over the cardiac cycle. The method was tested on 15 healthy volunteers with 4d ground truth landmarks and 5 pathological patients, all candidates for cardiac resynchronisation therapy. Visually significant differences between normal and pathological subjects in terms of synchrony between the regions were obtained, which enables us to distinguish between healthy and unhealthy subjects. The results indicate that the method may be promising for analysing cardiac function.keywordscardiac magnetic resonancecardiac resynchronisation therapyheart failure patientleft bundle branch blockcardiac motionthese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Published: 2013
Full Text: View/download PDF

12. Spatio-temporal dimension reduction of cardiac motion for group-wise analysis and statistical testing

Author: Kristin McLeod, Maxime Sermesant, Christof Seiler, Xavier Pennec, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, This project was partially funded by the Care4Me ITEA2 project and the Swiss National Science Foundation., and Kensaku Mori and Ichiro Sakuma and Yoshinobu Sato and Christian Barillot and Nassir Navab
Subjects: Data Interpretation, Population, 02 engineering and technology, Heart Ventricles/diagnostic imaging, Sensitivity and Specificity, Motion (physics), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Motion, 0302 clinical medicine, Spatio-Temporal Analysis, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Match moving, Reference Values, Singular value decomposition, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 0202 electrical engineering, electronic engineering, information engineering, Computer-Assisted/methods, Humans, Ventricular Function, Computer vision, education, Image Interpretation, Statistical hypothesis testing, Mathematics, Ultrasonography, Myocardial Contraction/physiology, Ground truth, education.field_of_study, business.industry, Dimensionality reduction, Left/physiology, Image Interpretation, Computer-Assisted/methods, Reproducibility of Results, Statistical model, Statistical, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Data Interpretation, Statistical, 020201 artificial intelligence & image processing, Artificial intelligence, Ventricular Function, Left/physiology, Image Enhancement/methods, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms
Abstract: International audience; Given the observed abnormal motion dynamics of patients with heart conditions, quantifying cardiac motion in both normal and pathological cases can provide useful insights for therapy planning. In order to be able to analyse the motion over multiple subjects in a ro- bust manner, it is desirable to represent the motion by a low number of parameters. We propose a reduced order cardiac motion model, reduced in space through a polyaffine model, and reduced in time by statistical model order reduction. The method is applied to a data-set of synthetic cases with known ground truth to validate the accuracy of the left ven- tricular motion tracking, and to validate a patient-specific reduced-order motion model. Population-based statistics are computed on a set of 15 healthy volunteers to obtain separate spatial and temporal bases. Re- sults demonstrate that the reduced model can efficiently detect abnor- mal motion patterns and even allowed to retrospectively reveal abnormal unnoticed motion within the control subjects.
Published: 2013
Full Text: View/download PDF

13. Proceedings of the MICCAI Challenge on Multimodal Brain Tumor Image Segmentation (BRATS) 2012

Author: Menze, Bjoern, Jakab, Andras, Bauer, Stefan, Reyes, Mauricio, Prastawa, Marcel, van Leemput, Koen, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Computational Image Analysis and Radiology (CIR lab), Medizinische Universität Wien = Medical University of Vienna, Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Scientific Computing and Imaging Institute (SCI Institute), University of Utah, Computer Science and Artificial Intelligence Laboratory [Cambridge] (CSAIL), Massachusetts Institute of Technology (MIT), Department of radiology (Massachusetts General Hospital), Massachusetts General Hospital [Boston], and Bjoern Menze and Andras Jakab and Mauricio Reyes and Stefan Bauer and Marcel Prastawa and Koen Van Leemput
Subjects: [SCCO.COMP]Cognitive science/Computer science
Abstract: International audience; Because of their unpredictable appearance and shape, segmenting brain tumors from multi-modal imaging data is one of the most challenging tasks in medical image analysis. Although many different segmentation strategies have been proposed in the literature, it is hard to compare existing methods because the validation datasets that are used differ widely in terms of input data (structural MR contrasts; perfusion or diffusion data; ...), the type of lesion (primary or secondary tumors; solid or infiltratively growing), and the state of the disease (pre- or post-treatment). In order to gauge the current state-of-the-art in automated brain tumor segmentation and compare between different methods, we are organizing a Multimodal Brain Tumor Segmentation (BRATS) challenge that is held in conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2012) on October 1st, 2012 in Nice, France.
Published: 2012

14. Trees on Geometrical Deformations to Model the Statistical Variability of Organs in Medical Images

Author: Seiler, Christof, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Université Nice Sophia Antipolis, Universität Bern, Mauricio Reyes and Xavier Pennec, and Asclepios, Project-Team
Subjects: Polyaffine registration, Shape statistics, Statistiques de forme, Modèle statistique génératif, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Transformations log-euclidiennes polyaffines, Arbres multi-échelles et hiérarchiques, Log-Euclidean polyaffine transformations, Recalage log-demons, Generative statistical model, Log-Demons registration, Bayesian registration, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Parametrization of diffeomorphisms, Recalage polyaffine, Recalage bayésien, Paramétrisation des difféomorphismes, Multiscale and hierarchical trees
Abstract: In medical image analysis, geometrical deformations are used to model intersubject variability. In orthopaedic applications, the geometrical variability is usually observable across anatomical scales. For instance, anatomical differences between mandible bones of different patients can be found on a coarse scale, between the entire left or right side, or on a fine scale, between teeth. Each level of granularity has specific regions of interest in clinical applications. The challenge is to connect the geometrical deformations to clinical regions across scales. In this thesis, we present this connection by introducing structured diffeomorphic registration. At the core of our method is the parametrization of geometrical deformations with trees of locally affine transformations describing intersubject variability across scales. In a second step, we statistically model the deformation parameters in a population by formulating a generative statistical model. This model allows us to incorporate deformation statistics as a prior in a Bayesian setting and it enables us to extend the classical sequential coarse to fine registration to a simultaneous optimization of all scales. This kind of group level prior is natural in a polyaffine context, if we assume one configuration of regions that describes an entire group of images with varying transformations for each region. We validate our approach on a wide range of orthopaedic applications: population-based implant design, biomechanical simulations and allograft selection for femur and mandibles. The improved intelligibility for clinicians and accuracy makes our method a good candidate for clinical use., Dans l'analyse d'images médicales, les déformations géométriques sont utilisées pour modéliser la variabilité entre les patients. Dans les applications orthopédiques, la variabilité géométrique est habituellement observable à différentes échelles. Dans le cas des os mandibulaires, par exemple, on observe des différences anatomiques entre le côté gauche et droit sur une échelle grossière, ou entre les dents sur une échelle plus fine. Chaque niveau de granularité contient des régions d'intérêt pour les applications cliniques. La difficulté est de relier les déformations géométriques avec les régions d'intérêt pour chaque type d'échelles. Dans cette thèse, nous présentons cette liaison par l'introduction du recalage difféomorphe et structuré. Le coeur de notre méthode est le paramétrage des déformations géométriques avec des arbres de transformations localement affines qui décrivent la variabilité entre les patients. En second lieu, nous modélisons statistiquement les paramètres de déformations dans une population par la formulation d'un modèle statistique génératif. Cette méthode nous permet d'intégrer des statistiques de déformations comme une probabilité a priori dans un cadre Bayésien et elle nous permet d'étendre le recalage classique d'un schéma grossier à un schéma fin avec une optimisation simultanée pour toutes les échelles. Nous validons notre approche sur plusieurs applications orthopédiques: la conception des implants pour une population, des simulations biomécaniques et la sélection d'allogreffes. L'amélioration de l'intelligibilité pour les cliniciens et de la précision obtenue fait de notre méthode un candidat prometteur pour des usages cliniques.
Published: 2012

15. A Near-Incompressible Poly-Affine Motion Model for Cardiac Function Analysis

Author: Xavier Pennec, Christof Seiler, Maxime Sermesant, Kristin McLeod, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), and University of Bern
Subjects: Cardiac function curve, Cardiac cycle, Computer science, Work (physics), Optical flow, Motion (geometry), Geometry, Deformation (meteorology), [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Range (mathematics), symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Jacobian matrix and determinant, symbols, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: Understanding the motion of the heart through the cardiac cycle can give useful insight for a range of different pathologies. In particular, quantifying regional cardiac motion can help clinicians to better determine cardiac function by identifying regions of thickened, ischemic or infarcted tissue. In this work we propose a method for cardiac motion analysis to track the deformation of the left ventricle at a regional level. This method estimates the affine motion of distinct regions of the myocardium using a near incompressible non-rigid registration algorithm based on the demon’s optical flow approach. The global motion over the ventricle is computed by a smooth fusion of the deformation in each segment using an anatomically aware poly-affine model for the heart. We apply the proposed method to a data-set of 10 volunteers. The results indicate that we are able to extract reasonably realistic deformation fields parametrised by a significantly reduced number of parameters compared to voxel-wise methods, which better enables for statistical analyses of the motion.keywordsmotion modelcardiac motionbinary maskjacobian determinantleft ventricle segmentationthese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Published: 2012
Full Text: View/download PDF

16. Capturing the Multiscale Anatomical Shape Variability with Polyaffine Transformation Trees

Author: Christof Seiler, Mauricio Reyes, Xavier Pennec, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), and University of Bern
Subjects: Mandibular Fractures/diagnostic imaging, 02 engineering and technology, 030218 nuclear medicine & medical imaging, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Mandibular Fractures, Prosthesis Fitting, Prosthesis Fitting/methods, Radiography, Dental, Computer-Assisted/methods, 0202 electrical engineering, electronic engineering, information engineering, Radiographic Image Enhancement/methods, Computer vision, Radiography, Dental/methods, Mathematics, Ground truth, education.field_of_study, Radiological and Ultrasound Technology, Basis (linear algebra), Radiographic Image Interpretation, Computer Graphics and Computer-Aided Design, Radiographic Image Enhancement, Tree (data structure), Surgery, Computer-Assisted, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Radiographic Image Interpretation, Computer-Assisted, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, Population, Health Informatics, Sensitivity and Specificity, Set (abstract data type), 03 medical and health sciences, Radiographic Image Interpretation, Computer-Assisted/methods, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Radiology, Nuclear Medicine and imaging, education, Cluster analysis, Dental/methods, Dental Implants, Surgery, Computer-Assisted/methods, business.industry, Reproducibility of Results, Pattern recognition, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Radiography, Transformation (function), Surgery, Artificial intelligence, Affine transformation, business
Abstract: International audience; Mandible fractures are classified depending on their location. In clinical practice, locations are grouped into regions at different scales according to anatomical, functional and esthetic considerations. Implant design aims at defining the optimal implant for each patient. Emerging population-based techniques analyze the anatomical variability across a population and perform statistical analysis to identify an optimal set of implants. Current efforts are focused on finding clusters of patients with similar characteristics and designing one implant for each cluster. Ideally, the description of anatomical variability is directly connected to the clinical regions. This connection is what we present here, by introducing a new registration method that builds upon a tree of locally affine transformations that describes variability at different scales. We assess the accuracy of our method on 146 CT images of femurs. Two medical experts provide the ground truth by manually measuring six landmarks. We illustrate the clinical importance of our method by clustering 43 CT images of mandibles for implant design. The presented method does not require any application-specific input, which makes it attractive for the analysis of other multiscale anatomical structures. At the core of our new method lays the introduction of a new basis for stationary velocity fields. This basis has very close links to anatomical substructures. In the future, this method has the potential to discover the hidden and possibly sparse structure of the anatomy.
Published: 2012
Full Text: View/download PDF

17. Simultaneous Multiscale Polyaffine Registration by Incorporating Deformation Statistics

Author: Xavier Pennec, Christof Seiler, Mauricio Reyes, Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Data Interpretation, Automated/methods, Physics::Medical Physics, Image registration, Context (language use), 02 engineering and technology, Mandible, Pattern Recognition, Sensitivity and Specificity, Pattern Recognition, Automated, Normal distribution, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Statistics, Image Interpretation, Computer-Assisted, 0202 electrical engineering, electronic engineering, information engineering, Maximum a posteriori estimation, Radiography, Dental, Computer-Assisted/methods, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, 610 Medicine & health, Image Interpretation, Moore–Penrose pseudoinverse, Radiography, Dental/methods, Mathematics, Dental/methods, Pattern Recognition, Automated/methods, Image Interpretation, Computer-Assisted/methods, Reproducibility of Results, Statistical model, Statistical, Image Enhancement, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Maxima and minima, Radiography, Data Interpretation, Statistical, Mandible/diagnostic imaging, Subtraction Technique, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 570 Life sciences, biology, 020201 artificial intelligence & image processing, Affine transformation, Image Enhancement/methods, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery, Algorithms
Abstract: PMID 23286041; International audience; Locally affine (polyaffine) image registration methods capture intersubject non-linear deformations with a low number of parameters, while providing an intuitive interpretation for clinicians. Considering the mandible bone, anatomical shape differences can be found at different scales, e.g. left or right side, teeth, etc. Classically, sequential coarse to fine registration are used to handle multiscale deformations, instead we propose a simultaneous optimization of all scales. To avoid local minima we incorporate a prior on the polyaffine transformations. This kind of groupwise registration approach is natural in a polyaffine context, if we assume one configuration of regions that describes an entire group of images, with varying transformations for each region. In this paper, we reformulate polyaffine deformations in a generative statistical model, which enables us to incorporate deformation statistics as a prior in a Bayesian setting. We find optimal transformations by optimizing the maximum a posteriori probability. We assume that the polyaffine transformations follow a normal distribution with mean and concentration matrix. Parameters of the prior are estimated from an initial coarse to fine registration. Knowing the region structure, we develop a blockwise pseudoinverse to obtain the concentration matrix. To our knowledge, we are the first to introduce simultaneous multiscale optimization through groupwise polyaffine registration. We show results on 42 mandible CT images.
Published: 2012
Full Text: View/download PDF

18. Statistical Model Based Shape Prediction from a Combination of Direct Observations and Various Surrogates

Author: Blanc, R., Seiler, Christof, Székely, G., Nolte, L., Reyes, Mauricio, Computer Vision Laboratory - ETHZ [Zurich], Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, and Asclepios, Project-Team
Subjects: [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; In computer-assisted orthopaedic surgery, recovering three-dimensional patient-specific anatomy from incomplete information has been focus of interest due to several factors such as less invasive surgical procedures, reduced radiation doses, and rapid intra-operative updates of the anatomy. The aim of this paper is to report results obtained combining statistical shape modeling and multivariate regression techniques for predicting bone shape from clinically and surgically relevant predictors, including sparse observations of the bone surface but also morphometric and anthropometric information. Different state of the art methods such as partial least square regression, principal component regression, canonical correlation analysis, and non-parametric kernel-based regression are compared. Clinically relevant surrogate variables and combinations are investigated on a database of 142 femur and 154 tibia shapes obtained from CT images. The results are evaluated using cross validation to quantify the prediction error. The proposed approach enables to characterize the added value of different predictors in a quantitative and localized fashion. Results indicate that complementary sources of information can be efficiently exploited to improve the accuracy of shape prediction.
Published: 2012

19. Population-based design of mandibular plates based on bone quality and morphology

Author: Mauricio Reyes, Habib Bousleiman, Christof Seiler, Tateyuki Iizuka, Lutz-Peter Nolte, Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Cranio-Maxillofacial Surgery [Bern], and Nicholas Ayache and Hervé Delingette and Polina Golland and Kensaku Mori
Subjects: Models, Anatomic, Male, Tomography, X-Ray Computed/methods, Dentistry, Design strategy, Bending, Population based, Internal/methods, Fixation (surgical), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Fracture Fixation, Models, Bone quality, Bone plate, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Orthopedics/methods, Computer Simulation, Fracture Fixation, Internal/methods, 610 Medicine & health, Tomography, Aged, Models, Statistical, business.industry, Anatomic, Bone and Bones/diagnostic imaging, Middle Aged, Statistical, Computational anatomy, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Internal Fixators, X-Ray Computed/methods, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Mandible/diagnostic imaging, 570 Life sciences, biology, Female, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Bone Plates, Intensity (heat transfer), Algorithms, Software, Biomedical engineering
Abstract: International audience; In this paper we present a new population-based implant design methodology, which advances the state-of-the-art approaches by combining shape and bone quality information into the design strategy. The method enhances the mechanical stability of the fixation and reduces the intra-operative in-plane bending which might impede the functionality of the locking mechanism. The method is presented for the case of mandibular locking fixation plates, where the mandibular angle and the bone quality at screw locations are taken into account. Using computational anatomy techniques, the method automatically derives, from a set of computed tomography images, the mandibular angle and the bone thickness and intensity values at the path of every screw. An optimisation strategy is then used to optimise the two parameters of plate angle and screw position. Results for the new design are presented along with a comparison with a commercially available mandibular locking fixation plate. A statistically highly significant improvement was observed. Our experiments allowed us to conclude that an angle of 126 degrees and a screw separation of 8 mm is a more suitable design than the standard 120 degrees and 9 mm.
Published: 2012
Full Text: View/download PDF

20. 3D Model-based Reconstruction of the Proximal Femur from Low-dose Biplanar X-Ray Images

Author: Nikos Paragios, Guoyan Zheng, Haithem Boussaid, Samuel Kadoury, Jean-Yves Lazennec, Iasonas Kokkinos, Organ Modeling through Extraction, Representation and Understanding of Medical Image Content (GALEN), Ecole Centrale Paris-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec, Philips Research North America, Philips Healthcare, CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, oey, Jesse and McKenna, Stephen and Trucco, Emanuele, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Ecole Centrale Paris, and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)
Subjects: Surface (mathematics), business.industry, Computer science, 0206 medical engineering, 02 engineering and technology, Function (mathematics), 3D modeling, 020601 biomedical engineering, 030218 nuclear medicine & medical imaging, Term (time), 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Segmentation, Computer vision, Minification, Artificial intelligence, business, Focus (optics), Projection (set theory)
Abstract: International audience; The 3D modeling of the proximal femur is a valuable diagnostic tool for orthopedic surgery planning. The use of computed tomography is the most prominent modality to visualize bones both in terms of resolution as well as in term of bone/tissue separation. Towards reducing the impact of radiation to the patient, low-dose X-ray imaging systems have been introduced while still providing partial views with rather low signal-to-noise ratio. In this paper, we focus on automating the 3D proximal femur reconstruction from simultaneously acquired 2D views. A deformable model represented by triangulated mesh surfaces extends to a linear sub-space describing the variations across individuals. Segmentation consists of inferring a global deformation of 3D model followed by a local adaption based on the most prominent combination of the sub-space parameters. The ba-sis of which relies on the minimization of a cost function based on the biplanar projection of this model. To this end, we employ an active region model that aims at optimizing the 3D model parameters such that projection of surface is attracted from edge potentials, while creating an optimal partition between the bone class and the remaining structures. The global parameters of the model and the local ones are optimized through a gradient-free approach. Promising results demonstrate the potentials of our method compared to a supervised reconstruction technique.
Published: 2011
Full Text: View/download PDF

21. Mesh-based vs. Image-based Statistical Model of Appearance of the Human Femur: A Preliminary Comparison Study for the Creation of Finite Element Meshes

Author: Bonaretti, S, Seiler, Christof, Boichon, C, Büchler, P, Reyes, Mauricio, Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), ANSYS France, ANSYS, and Asclepios, Project-Team
Subjects: [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, ComputingMethodologies_COMPUTERGRAPHICS
Abstract: International audience; Statisticalmodelshavebeenrecentlyintroducedincomputational orthopaedics to investigate the bone mechanical properties across several populations. A fundamental aspect for the construction of statistical models concerns the establishment of accurate anatomical correspondences among the objects of the training dataset. Various methods have been proposed to solve this problem such as mesh morphing or image registration algorithms. The objective of this study is to compare a mesh-based and an image-based statistical appearance model approaches for the creation of finite element(FE) meshes. A computer tomography (CT) dataset of 157 human left femurs was used for the comparison. For each approach, 30 finite element meshes were generated with the models. The quality of the obtained FE meshes was evaluated in terms of volume, size and shape of the elements. Results showed that the quality of the meshes obtained with the image-based approach was higher than the quality of the mesh-based approach. Future studies are required to evaluate the impact of this finding on the final mechanical simulations.
Published: 2011

22. Femur specific polyaffine model to regularize the log-domain demons registration

Author: Seiler, Christof, Pennec, Xavier, Ritacco, Lucas, Reyes, Mauricio, Dawant, Benoit M., Haynor, David R., Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Italian Hospital of Buenos Aires, Dawant, Benoit M. and Haynor, and David R.
Subjects: Ground truth, Fine-tuning, business.industry, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Gaussian blur, Regularization (mathematics), [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, symbols, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 570 Life sciences, biology, Computer vision, Femur, Affine transformation, Artificial intelligence, Performance improvement, 610 Medicine & health, business, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Smoothing
Abstract: International audience; Osteoarticular allograft transplantation is a popular treatment method in wide surgical resections with large defects. For this reason hospitals are building bone data banks. Performing the optimal allograft selection on bone banks is crucial to the surgical outcome and patient recovery. However, current approaches are very time consuming hindering an efficient selection. We present an automatic method based on registration of femur bones to overcome this limitation. We introduce a new regularization term for the log-domain demons algorithm. This term replaces the standard Gaussian smoothing with a femur specific polyaffine model. The polyaffine femur model is constructed with two affine (femoral head and condyles) and one rigid (shaft) transformation. Our main contribution in this paper is to show that the demons algorithm can be improved in specific cases with an appropriate model. We are not trying to find the most optimal polyaffine model of the femur, but the simplest model with a minimal number of parameters. There is no need to optimize for different number of regions, boundaries and choice of weights, since this fine tuning will be done automatically by a final demons relaxation step with Gaussian smoothing. The newly developed synthesis approach provides a clear anatomically motivated modeling contribution through the specific three component transformation model, and clearly shows a performance improvement (in terms of anatomical meaningful correspondences) on 146 CT images of femurs compared to a standard multiresolution demons. In addition, this simple model improves the robustness of the demons while preserving its accuracy. The ground truth are manual measurements performed by medical experts.
Published: 2011
Full Text: View/download PDF

23. Geometry-Aware Multiscale Image Registration Via OBBTree-Based Polyaffine Log-Demons

Author: Christof Seiler, Mauricio Reyes, Xavier Pennec, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, and This work has been funded by the Swiss National Science Foundation.
Subjects: Logarithm, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Image Processing, Physics::Medical Physics, Tomography, X-Ray Computed/methods, 02 engineering and technology, Mandible, Regularization (mathematics), Diagnostic Imaging/methods, 0302 clinical medicine, Theoretical, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Models, 0202 electrical engineering, electronic engineering, information engineering, Image Processing, Computer-Assisted, Computer-Assisted/methods, Orthopedics/methods, Computer vision, Tomography, Mathematics, education.field_of_study, Statistical, X-Ray Computed/methods, Mandible/diagnostic imaging, 020201 artificial intelligence & image processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, Diagnostic Imaging, Image Processing, Computer-Assisted/methods, Population, Image registration, Image (mathematics), 03 medical and health sciences, Robustness (computer science), Image Interpretation, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, education, Image Interpretation, Models, Statistical, business.industry, Image Interpretation, Computer-Assisted/methods, Models, Theoretical, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Maxima and minima, Orthopedics, Subtraction Technique, Affine transformation, Artificial intelligence, business, Tomography, X-Ray Computed, 030217 neurology & neurosurgery, Software
Abstract: International audience; Non-linear image registration is an important tool in many areas of image analysis. For instance, in morphometric studies of a population of brains, free-form deformations between images are analyzed to describe the structural anatomical variability. Such a simple deformation model is justified by the absence of an easy expressible prior about the shape changes. Applying the same algorithms used in brain imaging to orthopedic images might not be optimal due to the difference in the underlying prior on the inter-subject deformations. In particular, using an un-informed deformation prior often leads to local minima far from the expected solution. To improve robustness and promote anatomically meaningful deformations, we propose a locally affine and geometry-aware registration algorithm that automatically adapts to the data. We build upon the log-domain demons algorithm and introduce a new type of OBBTree-based regularization in the registration with a natural multiscale structure. The regularization model is composed of a hierarchy of locally affine transformations via their logarithms. Experiments on mandibles show improved accuracy and robustness when used to initialize the demons, and even similar performance by direct comparison to the demons, with a significantly lower degree of freedom. This closes the gap between polyaffine and non-rigid registration and opens new ways to statistically analyze the registration results.
Published: 2011
Full Text: View/download PDF

24. Parametric regression of 3D medical images through the exploration of non-parametric regression models

Author: Christof Seiler, Xavier Pennec, Mauricio Reyes, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), and University of Bern
Subjects: Multivariate statistics, Polynomial, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer science, Univariate, Regression analysis, Predictor variables, computer.software_genre, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Regression, Nonparametric regression, Kernel (linear algebra), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Statistics, Parametric model, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Data mining, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, computer, Parametric statistics
Abstract: International audience; Currently there is an increase usage of CT-based bone diagnosis because low-radiation and cost-effective 2D imaging modalities do not provide the necessary 3D information for bone diagnosis. The fundamental objective of our work is to build a model connecting 2D X-ray information to 3D CT information through regression. As a first step we propose an univariate non-parametric regression on individual predictor variables to explore the non-linearity of the data. To later combine these univariate models we then replace them with parametric models. We examine two predictors, shaft length and caput collum diaphysis angle on a database of 182 CT images of femurs. We show that for each predictor it is possible to describe 99% of the variance through a simple up to second order parametric model. These findings will allow us to extend to the multivariate case in the future.
Published: 2010
Full Text: View/download PDF

25. Combined Statistical Model of Bone Shape and Mechanical Properties for Bone and Implant Modeling

Author: Kistler, Michael, Reyes, Mauricio, Seiler, Christof, Bonaretti, Serena, Büchler, Philippe, Asclepios, Project-Team, Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Seiler, Christof
Subjects: [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; Current design process for orthopedic implants relies on limited knowledge about shape and mechanical properties of the bones of interest. Nowadays implant design is based on reduced information deriving from literature (e.g. bone angles and lengths) and does not take into account differences among populations (ethnicity, height, gender ...). For these reasons we propose a method to model bone shape variations for different populations. The proposed model is based on recent advances in image processing to create compact mathematical representations of bone anatomy. About 140 CT images were collected. These images were segmented and registered to find the anatomical correspondences among them. Based on this registration, a statistical shape model was built to compute the average bone, variations of bone shape across the population and to generate new bone instances. The finite element method was then used to calculate the mechanical performance of the generated instances. The developed model was used to compare bone stiffness between male and female populations. Results showed no significant difference between male and female bone stiffness. This was affected by the fact that all instance had the same dimensions and the same mechanical properties. The combination of statistical shape model and finite element calculation has the potential to become a powerful tool to assess orthopedic procedures and implant design.
Published: 2010

26. Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

Author: Christof Seiler, Mauricio Reyes, Rasmus Reinhold Paulsen, Philippe Büchler, Lutz-Peter Nolte, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Department of Informatics and Mathematical Modelling [Lyngby] (IMM), Danmarks Tekniske Universitet = Technical University of Denmark (DTU), N. Magnenat-Thalmann, J. J. Zhang & Feng, D. D., Magnenat-Thalmann, Nadia, Zhang, Jian J, and Feng, David D
Subjects: Theoretical computer science, Random field, Markov random field, Markov chain, Continuum mechanics, Computer science, Graphics hardware, OpenGL, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 610 Medicine & health, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Finite element method, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 570 Life sciences, biology, Representation (mathematics), Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMethodologies_COMPUTERGRAPHICS
Abstract: Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU See Graphics processing unit ).
Published: 2009
Full Text: View/download PDF

27. Relation between the apparent modulus of human trabecular bone assessed by micro finite element analysis and the ultrasonic parameters

Author: Haiat, Guillaume, Padilla, F, Svrcekova, M., Chevalier, Y, Pahr, D., Laugier, P, Zysset, P., Lemaire, Thibault, Laboratoire de Modélisation et Simulation Multi Echelle (MSME), Université Paris-Est Marne-la-Vallée (UPEM)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Matériaux organiques à propriétés spécifiques (LMOPS), Laboratoire d'Electrochimie et de Physico-chimie des Matériaux et des Interfaces (LEPMI ), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Institut de Chimie du CNRS (INC)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Institut de Chimie du CNRS (INC)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Imagerie Biomédicale [Paris] (LIB), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Laboratoire d'Imagerie Biomédicale (LIB), Institut de Chimie du CNRS (INC)-Institut National Polytechnique de Grenoble (INPG)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Institut National Polytechnique de Grenoble (INPG)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Paris-Est Marne-la-Vallée (UPEM), Laboratoire matériaux organiques à propriétés spécifiques (LMOPS), Laboratoire d'Electrochimie et de Physico-chimie des Matériaux et des Interfaces (LEPMI), and Université Grenoble Alpes (UGA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SPI.MECA.BIOM] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], [SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2009

28. Conditional Variability of Statistical Shape Models Based on Surrogate Variables

Author: Christof Seiler, Gábor Székely, Rémi Blanc, Mauricio Reyes, Computer Vision Laboratory - ETHZ [Zurich], Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Asclepios, Project-Team
Subjects: Models, Anatomic, Physics::Medical Physics, Tomography, X-Ray Computed/methods, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Initialization, Femur/diagnostic imaging, 030218 nuclear medicine & medical imaging, Imaging, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 0302 clinical medicine, Heat kernel signature, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Models, Active shape model, Three-Dimensional/methods, Computer-Assisted/methods, Radiographic Image Enhancement/methods, Tomography, Mathematics, Anatomic, Radiographic Image Interpretation, Human femur, Statistical, X-Ray Computed/methods, Imaging, Three-Dimensional/methods, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, Shape analysis (digital geometry), Mathematical optimization, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Quantitative Biology::Tissues and Organs, Kernel density estimation, Automated/methods, Pattern Recognition, Effect Modifier, Epidemiologic, Models, Biological, Sensitivity and Specificity, 03 medical and health sciences, Radiographic Image Interpretation, Computer-Assisted/methods, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Models, Statistical, Epidemiologic, Pattern Recognition, Automated/methods, Reproducibility of Results, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Conditional probability distribution, Biological, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Effect Modifier, Point distribution model, 030217 neurology & neurosurgery
Abstract: International audience; We propose to increment a statistical shape model with surrogate variables such as anatomical measurements and patient-related information, allowing conditioning the shape distribution to follow prescribed anatomical constraints. The method is applied to a shape model of the human femur, modeling the joint density of shape and anatomical parameters as a kernel density. Results show that it allows for a fast, intuitive and anatomically meaningful control on the shape deformations and an effective conditioning of the shape distribution, allowing the analysis of the remaining shape variability and relations between shape and anatomy. The approach can be further employed for initializing elastic registration methods such as Active Shape Models, improving their regularization term and reducing the search space for the optimization.
Published: 2009

29. Relation between the ultrasonic parameters and strength of human cancellous bone: a numerical study

Author: Haiat, Guillaume, Padilla, F, Svrcekova, M., Chevalier, Y, Pahr, D., Laugier, P, Zysset, P., Laboratoire de Modélisation et Simulation Multi Echelle (MSME), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Paris-Est Marne-la-Vallée (UPEM), Laboratoire matériaux organiques à propriétés spécifiques (LMOPS), Laboratoire d'Electrochimie et de Physico-chimie des Matériaux et des Interfaces (LEPMI), Université Grenoble Alpes (UGA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Imagerie Biomédicale (LIB), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Université Paris-Est Marne-la-Vallée (UPEM)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Matériaux organiques à propriétés spécifiques (LMOPS), Laboratoire d'Electrochimie et de Physico-chimie des Matériaux et des Interfaces (LEPMI ), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Institut de Chimie du CNRS (INC)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Institut de Chimie du CNRS (INC)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Imagerie Biomédicale [Paris] (LIB), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut de Chimie du CNRS (INC)-Institut National Polytechnique de Grenoble (INPG)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Institut National Polytechnique de Grenoble (INPG)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), and Lemaire, Thibault
Subjects: [SPI.MECA.BIOM] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], [SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2008

30. Relationship between the ultrasonic parameters and apparent modulus of human cancellous bone assessed by micro finite element analysis

Author: Haiat, Guillaume, Padilla, F, Svrcekova, M., Chevalier, Y., Pahr, D., Laugier, P, Zysset, P., Lemaire, Thibault, Laboratoire de Modélisation et Simulation Multi Echelle (MSME), Université Paris-Est Marne-la-Vallée (UPEM)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'automatique et de génie des procédés (LAGEP), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-École Supérieure Chimie Physique Électronique de Lyon-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Imagerie Biomédicale (LIB), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Paris-Est Marne-la-Vallée (UPEM), Université de Lyon-Université de Lyon-École Supérieure de Chimie Physique Électronique de Lyon (CPE)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Laboratoire d'Imagerie Biomédicale [Paris] (LIB)
Subjects: [SPI.MECA.BIOM] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], [SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2008

31. Numerical study of the dependence of the ultrasonic parameters on apparent modulus of human cancellous bone assessed by micro finite element analysis

Author: Haiat, Guillaume, Padilla, F, Svrcekova, M., Chevalier, Y, Pahr, D., Laugier, P, Zysset, P., Lemaire, Thibault, Laboratoire de Modélisation et Simulation Multi Echelle (MSME), Université Paris-Est Marne-la-Vallée (UPEM)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Matériaux organiques à propriétés spécifiques (LMOPS), Laboratoire d'Electrochimie et de Physico-chimie des Matériaux et des Interfaces (LEPMI ), Institut de Chimie du CNRS (INC)-Institut National Polytechnique de Grenoble (INPG)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Institut National Polytechnique de Grenoble (INPG)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Imagerie Biomédicale (LIB), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Institut de Chimie du CNRS (INC)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Institut de Chimie du CNRS (INC)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Paris-Est Marne-la-Vallée (UPEM), Laboratoire matériaux organiques à propriétés spécifiques (LMOPS), Laboratoire d'Electrochimie et de Physico-chimie des Matériaux et des Interfaces (LEPMI), Université Grenoble Alpes (UGA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS), and Laboratoire d'Imagerie Biomédicale [Paris] (LIB)
Subjects: [SPI.MECA.BIOM] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], [SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2008

32. The European LGS test facility

Author: Richard M. Myers, Mark A. Harrison, Tim Morris, Simone Esposito, R. G. Talbot, Simon Tulloch, Jean-Paul Pique, Sebastian Rabien, Enrico Marchetti, Erez N. Ribak, Remko Stuik, H. Guillet de Chatellus, J. C. Guerra, Alexander V. Goncharov, Hermine Schnetler, Enrico Pinna, D. Bonaccini Calia, M. Reyes, Nicholas Devaney, R. G. M. Rutten, M. Strachan, S. J. Goodsell, R. Holzloehner, Department of Infectious Diseases, University of Naples Federico II, St Petersburg State University (SPbU), Laboratoire Interdisciplinaire de Physique [Saint Martin d’Hères] (LIPhy), Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF), Agenzia Spaziale Italiana (ASI), Institute for Surgical Technology and Biomechanics [Bern] (ISTB), and University of Bern
Subjects: Wavefront, [PHYS]Physics [physics], [PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics], Test facility, business.industry, Computer science, 02 engineering and technology, 021001 nanoscience & nanotechnology, Laser, 7. Clean energy, 01 natural sciences, law.invention, 010309 optics, Telescope, Laser guide star, law, 0103 physical sciences, William Herschel Telescope, Extremely Large Telescope, Aerospace engineering, Elongation, 0210 nano-technology, business, Adaptive optics, Remote sensing
Abstract: International audience; A European Laser Guide Star (LGS) test facility is proposed for the 4.2m William Herschel Telescope (WHT) on La Palma. It will test the next-generation Adaptive Optics (AO) LGS technologies to aid risk mitigation of Extremely Large Telescope (ELT) LGS AO systems. In particular, critical scaling of current LGS AO technologies to ELT dimensions will be tested. For example, experiments addressing increased spot elongation, cone effect and the order of correction required. A pan-European consortium proposes to construct test facility infrastructure on the WHT for a number of risk mitigating experiments. The infrastructure includes the construction of a Nasmyth platform based controlled environment 'Ground-based Adaptive optics Innovative Laboratory' (GRAIL), an experimental test environment 'Testbed integration facility' (TIF) and some common-experiment equipment such as the Common Re-Imaging AO System. Experiments that are proposed for this facility cover the areas of laser technologies, spot elongation, LGS wavefront sensing, parallel launch concepts, Multi-Object AO, atmospheric characterisation, co-phasing and real-time control system risk mitigation.
Published: 2007
Full Text: View/download PDF

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources

Refine your results

32 results on '"Institute for Surgical Technology and Biomechanics [Bern] (ISTB)"'

1. Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

2. Compressive behaviour of uniaxially aligned individual mineralised collagen fibres at the micro- and nanoscale

3. Mechanical restoration and failure analyses of a hydrogel and scaffold composite strategy for annulus fibrosus repair

4. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

5. Why rankings of biomedical image analysis competitions should be interpreted with care

6. MICROCRACK EVOLUTION IN MICROINDENTATION OF OVINE CORTCAL BONE INVESTIGATED WITH TIME LAPSED SRµCT

7. Characterizing microcrack orientation distribution functions in osteonal bone samples

8. The JEM-EUSO mission : an introduction

9. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

10. Proceedings of the MICCAI Challenge on Multimodal Brain Tumor Image Segmentation (BRATS) 2013

11. Regional Analysis of Left Ventricle Function using a Cardiac-Specific Polyaffine Motion Model

12. Spatio-temporal dimension reduction of cardiac motion for group-wise analysis and statistical testing

13. Proceedings of the MICCAI Challenge on Multimodal Brain Tumor Image Segmentation (BRATS) 2012

14. Trees on Geometrical Deformations to Model the Statistical Variability of Organs in Medical Images

15. A Near-Incompressible Poly-Affine Motion Model for Cardiac Function Analysis

16. Capturing the Multiscale Anatomical Shape Variability with Polyaffine Transformation Trees

17. Simultaneous Multiscale Polyaffine Registration by Incorporating Deformation Statistics

18. Statistical Model Based Shape Prediction from a Combination of Direct Observations and Various Surrogates

19. Population-based design of mandibular plates based on bone quality and morphology

20. 3D Model-based Reconstruction of the Proximal Femur from Low-dose Biplanar X-Ray Images

21. Mesh-based vs. Image-based Statistical Model of Appearance of the Human Femur: A Preliminary Comparison Study for the Creation of Finite Element Meshes

22. Femur specific polyaffine model to regularize the log-domain demons registration

23. Geometry-Aware Multiscale Image Registration Via OBBTree-Based Polyaffine Log-Demons

24. Parametric regression of 3D medical images through the exploration of non-parametric regression models

25. Combined Statistical Model of Bone Shape and Mechanical Properties for Bone and Implant Modeling

26. Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

27. Relation between the apparent modulus of human trabecular bone assessed by micro finite element analysis and the ultrasonic parameters

28. Conditional Variability of Statistical Shape Models Based on Surrogate Variables

29. Relation between the ultrasonic parameters and strength of human cancellous bone: a numerical study

30. Relationship between the ultrasonic parameters and apparent modulus of human cancellous bone assessed by micro finite element analysis

31. Numerical study of the dependence of the ultrasonic parameters on apparent modulus of human cancellous bone assessed by micro finite element analysis

32. The European LGS test facility

Catalog

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Database

Publisher

32 results on '"Institute for Surgical Technology and Biomechanics [Bern] (ISTB)"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources