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13 results on '"Carbayo A"'

5. Right ventricle segmentation from cardiac MRI: A collation study

Author

Petitjean, Caroline, Zuluaga, Maria A., Bai, Wenjia, Dacher, Jean-Nicolas, Grosgeorge, Damien, Caudron, Jérôme, Ruan, Su, Ayed, Ismail Ben, Cardoso, M. Jorge, Chen, Hsiang-Chou, Jimenez-Carretero, Daniel, Ledesma-Carbayo, Maria J., Davatzikos, Christos, Doshi, Jimit, Erus, Guray, Maier, Oskar M.O., Nambakhsh, Cyrus M.S., Ou, Yangming, Ourselin, Sébastien, Peng, Chun-Wei, Peters, Nicholas S., Peters, Terry M., Rajchl, Martin, Rueckert, Daniel, Santos, Andres, Shi, Wenzhe, Wang, Ching-Wei, Wang, Haiyan, and Yuan, Jing

Published
2015
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6. Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

Author

Rudyanto, Rina D., Kerkstra, Sjoerd, van Rikxoort, Eva M., Fetita, Catalin, Brillet, Pierre-Yves, Lefevre, Christophe, Xue, Wenzhe, Zhu, Xiangjun, Liang, Jianming, Öksüz, İlkay, Ünay, Devrim, Kadipaşaogˇlu, Kamuran, Estépar, Raúl San José, Ross, James C., Washko, George R., Prieto, Juan-Carlos, Hoyos, Marcela Hernández, Orkisz, Maciej, Meine, Hans, Hüllebrand, Markus, Stöcker, Christina, Mir, Fernando Lopez, Naranjo, Valery, Villanueva, Eliseo, Staring, Marius, Xiao, Changyan, Stoel, Berend C., Fabijanska, Anna, Smistad, Erik, Elster, Anne C., Lindseth, Frank, Foruzan, Amir Hossein, Kiros, Ryan, Popuri, Karteek, Cobzas, Dana, Jimenez-Carretero, Daniel, Santos, Andres, Ledesma-Carbayo, Maria J., Helmberger, Michael, Urschler, Martin, Pienn, Michael, Bosboom, Dennis G.H., Campo, Arantza, Prokop, Mathias, de Jong, Pim A., Ortiz-de-Solorzano, Carlos, Muñoz-Barrutia, Arrate, and van Ginneken, Bram

Published
2014
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9. Autocalibration method for non-stationary CT bias correction

Author

Gonzalo Vegas-Sánchez-Ferrero, Raúl San José Estépar, George R. Washko, and Maria J. Ledesma-Carbayo

Subjects
Computer science, Partial volume, Health Informatics, Computed tomography, Iterative reconstruction, Signal-To-Noise Ratio, Radiation Dosage, Signal, Imaging phantom, Article, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Signal-to-noise ratio, Calibration, medicine, Humans, Radiology, Nuclear Medicine and imaging, Computer vision, Lung, Observer Variation, Radiological and Ultrasound Technology, medicine.diagnostic_test, business.industry, Phantoms, Imaging, Computer Graphics and Computer-Aided Design, Noise, 030220 oncology & carcinogenesis, Radiographic Image Interpretation, Computer-Assisted, Computer Vision and Pattern Recognition, Artificial intelligence, business, Tomography, X-Ray Computed, Algorithms
Abstract

Computed tomography (CT) is a widely used imaging modality for screening and diagnosis. However, the deleterious effects of radiation exposure inherent in CT imaging require the development of image reconstruction methods which can reduce exposure levels. The development of iterative reconstruction techniques is now enabling the acquisition of low-dose CT images whose quality is comparable to that of CT images acquired with much higher radiation dosages. However, the characterization and calibration of the CT signal due to changes in dosage and reconstruction approaches is crucial to provide clinically relevant data. Although CT scanners are calibrated as part of the imaging workflow, the calibration is lim-ited to select global reference values and does not consider other inherent factors of the acquisition that depend on the subject scanned (e.g. photon starvation, partial volume effect, beam hardening) and result in a non-stationary noise response. In this work, we analyze the effect of reconstruction biases caused by non-stationary noise and propose an autocalibration methodology to compensate it. Our contributions are: 1) the derivation of a functional relationship between observed bias and non-stationary noise, 2) a robust and accurate method to estimate the local variance, 3) an autocalibration methodology that does not necessarily rely on a calibration phantom, attenuates the bias caused by noise and removes the sys-tematic bias observed in devices from different vendors. The validation of the proposed methodology was performed with a physical phantom and clinical CT scans acquired with different configurations (kernels, doses, algorithms including iterative reconstruction). The results confirmed the suitability of the proposed methods for removing the intra-device and inter-device reconstruction biases.

Published
2017

10. Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: The VESSEL12 study

Author

Pim A. de Jong, Raúl San José Estépar, Marius Staring, Valery Naranjo, Daniel Jimenez-Carretero, Christina Stöcker, Mathias Prokop, Maciej Orkisz, Catalin Fetita, Christophe Lefevre, Arantza Campo, Changyan Xiao, Bram van Ginneken, Karteek Popuri, Wenzhe Xue, Erik Smistad, Dennis Bosboom, Andres Santos, Michael Pienn, Carlos Ortiz-de-Solorzano, Michael Helmberger, Martin Urschler, James C. Ross, Kamuran A. Kadipasaoglu, Dana Cobzas, Devrim Unay, Rina D. Rudyanto, Hans Meine, Ryan Kiros, Eva M. van Rikxoort, Frank Lindseth, Markus Hüllebrand, Berend C. Stoel, Maria J. Ledesma-Carbayo, Amir Hossein Foruzan, Sjoerd Kerkstra, George R. Washko, Arrate Muñoz-Barrutia, Pierre Yves Brillet, Xiangjun Zhu, Anna Fabijańska, Ilkay Oksuz, Fernando L opez Mir, Eliseo Villanueva, Anne C. Elster, Juan Carlos Prieto, Marcela Hernández Hoyos, Jianming Liang, Center for Applied Medical Research [Plamplona] (CIMA), Universidad de Navarra [Pamplona] (UNAV), Diagnostic Image Analysis Group [Nijmegen], Radboud University Medical Center [Nijmegen], Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS), Département Advanced Research And Techniques For Multidimensional Imaging Systems (ARTEMIS), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Hypoxie et Poumon : pneumopathologies fibrosantes, modulations ventilatoires et circulatoires (H&P), UFR SMBH-Université Paris 13 (UP13), Arizona State University [Tempe] (ASU), Equipe Image - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen (GREYC), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS), Electrical and Electronics Engineering [Istanbul], Bahcesehir University [Istanbul], Biomedical Engineering [Istanbul], Brigham and Women's Hospital [Boston], Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Universidad de los Andes [Bogota], Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales (MOTIVATE), Fraunhofer Institute for Medical Image Computing MEVIS [Bremen] (Fraunhofer MEVIS), Universidad Politécnica de Valencia, Instituto Interuniversitario de Investigacion en Bioingenieria Y Tecnologia Orientada Al Ser Humano, Division of image processing [Leiden], Leiden University Medical Center (LUMC)-Department of Radiology, Liaoning Technical University [Huludao], Łódź University of Technology, Norwegian University of Science and Technology [Trondheim] (NTNU), Shahed University [Téhéran], University of Alberta, Modeling, localization, recognition and interpretation in computer vision (MOVI), Graphisme, Vision et Robotique (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Biomedical Image Technologies, Biomedical Research Center (CIBER-BBN), Universidad Politécnica de Madrid (UPM), Instituto Nacional de Pesquisas da Amazônia (INPA), Institute for Computer Graphics and Vision [Graz] (ICG), Graz University of Technology [Graz] (TU Graz), Ludwig Boltzmann Institute for Clinical Forensic Imaging [Graz] (LBI-CFI), Ludwig Boltzmann Institute for Lung Vascular Research [Graz], Pulmonary Department [Pamplona], Universidad de Navarra [Pamplona] (UNAV)-Clínica Universidad de Navarra [Pamplona], Department of Radiology [Utrecht], University Medical Center [Utrecht], Université Paris 13 (UP13)-UFR SMBH, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU), Universidad de los Andes [Bogota] (UNIANDES), Fraunhofer Institute for Digital Medicine (Fraunhofer MEVIS), Fraunhofer (Fraunhofer-Gesellschaft), Universitat Politècnica de València (UPV), Norwegian University of Science and Technology (NTNU), Publica, Center for Applied Medical Research [Plamplona] ( CIMA ), Universidad de Navarra [Pamplona] ( UNAV ), Mathématiques Appliquées à Paris 5 ( MAP5 - UMR 8145 ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National des Sciences Mathématiques et de leurs Interactions-Centre National de la Recherche Scientifique ( CNRS ), Département Advanced Research And Techniques For Multidimensional Imaging Systems ( ARTEMIS ), Institut Mines-Télécom [Paris]-Télécom SudParis ( TSP ), Hypoxie et Poumon : pneumopathologies fibrosantes, modulations ventilatoires et circulatoires ( H&P ), Université Paris 13 ( UP13 ) -UFR SMBH, Arizona State University [Tempe] ( ASU ), Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen ( GREYC ), Université de Caen Normandie ( UNICAEN ), Normandie Université ( NU ) -Normandie Université ( NU ) -Ecole Nationale Supérieure d'Ingénieurs de Caen ( ENSICAEN ), Normandie Université ( NU ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Caen Normandie ( UNICAEN ), Normandie Université ( NU ) -Centre National de la Recherche Scientifique ( CNRS ), 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 ), 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 ), 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 ), Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales ( MOTIVATE ), Fraunhofer Institute for Medical Image Computing MEVIS [Bremen] ( Fraunhofer MEVIS ), Norwegian University of Science and Technology [Trondheim] ( NTNU ), University of Alberta [Edmonton], Modeling, localization, recognition and interpretation in computer vision ( MOVI ), Graphisme, Vision et Robotique ( GRAVIR - IMAG ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National Polytechnique de Grenoble ( INPG ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National Polytechnique de Grenoble ( INPG ) -Centre National de la Recherche Scientifique ( CNRS ) -Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ), Biomedical Image Technologies, Biomedical Research Center ( CIBER-BBN ), Universidad Politécnica de Madrid ( UPM ), Instituto Nacional de Pesquisas da Amazônia ( INPA ), Instituto Nacional de Pesquisas da Amazônia, Institute for Computer Graphics and Vision [Graz] ( ICG ), Graz University of Technology [Graz] ( TU Graz ), Ludwig Boltzmann Institute for Clinical Forensic Imaging [Graz] ( LBI-CFI ), and Universidad de Navarra [Pamplona] ( UNAV ) -Clínica Universidad de Navarra [Pamplona]

Subjects
Algorithm comparison, Vascular damage Radboud Institute for Health Sciences [Radboudumc 16], ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Contrast Media, Health Informatics, Computed tomography, Vessel segmentation, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, Sensitivity and Specificity, Article, Imaging modalities, Pattern Recognition, Automated, Segmentation, TEORIA DE LA SEÑAL Y COMUNICACIONES, Medical imaging, Medicine, Humans, Radiology, Nuclear Medicine and imaging, Challenge, Thoracic computed tomography, Lung, Lung vessels, Netherlands, Radiological and Ultrasound Technology, medicine.diagnostic_test, business.industry, Other Research Radboud Institute for Health Sciences [Radboudumc 0], Computer Graphics and Computer-Aided Design, Automation, Spain, Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], Computer-aided, Inflammatory diseases Radboud Institute for Health Sciences [Radboudumc 5], Radiographic Image Interpretation, Computer-Assisted, Computer Vision and Pattern Recognition, business, Tomography, X-Ray Computed, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, Rare cancers Radboud Institute for Health Sciences [Radboudumc 9]
Abstract

Contains fulltext : 137671.pdf (Publisher’s version ) (Open Access) The {VESSEL12} ({VES}sel {SE}mentation in the {L}ung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography ({CT}) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 {CT} scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the {VESSEL12} challenge, held at International Symposium on Biomedical Imaging ({ISBI}) 2012. All results have been published at the {VESSEL12} website http://{VESSEL12}.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.

Published
2014

11. Statistical characterization of noise for spatial standardization of CT scans: Enabling comparison with multiple kernels and doses

Author

Gonzalo Vegas-Sánchez-Ferrero, Raúl San José Estépar, Maria J. Ledesma-Carbayo, and George R. Washko

Subjects
Computer science, Calibration (statistics), Partial volume, Health Informatics, Iterative reconstruction, Signal-To-Noise Ratio, Radiation Dosage, 01 natural sciences, Sensitivity and Specificity, Imaging phantom, Article, 030218 nuclear medicine & medical imaging, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Humans, Radiology, Nuclear Medicine and imaging, Computer vision, 0101 mathematics, Telecomunicaciones, Radiological and Ultrasound Technology, business.industry, Phantoms, Imaging, Reproducibility of Results, Statistical model, Mixture model, Computer Graphics and Computer-Aided Design, Noise, Electrónica, Computer Vision and Pattern Recognition, Tomography, Artificial intelligence, business, Artifacts, Tomography, X-Ray Computed, Algorithms
Abstract

Computerized tomography (CT) is a widely adopted modality for analyzing directly or indirectly functional, biological and morphological processes by means of the image characteristics. However, the potential utilization of the information obtained from CT images is often limited when considering the analysis of quantitative information involving different devices, acquisition protocols or reconstruction algorithms. Although CT scanners are calibrated as a part of the imaging workflow, the calibration is circumscribed to global reference values and does not circumvent problems that are inherent to the imaging modality. One of them is the lack of noise stationarity, which makes quantitative biomarkers extracted from the images less robust and stable. Some methodologies have been proposed for the assessment of non-stationary noise in reconstructed CT scans. However, those methods focused on the non-stationarity only due to the reconstruction geometry and are mainly based on the propagation of the variance of noise throughout the whole reconstruction process. Additionally, the philosophy followed in the state-of-the-art methods is based on the reduction of noise, but not in the standardization of it. This means that, even if the noise is reduced, the statistics of the signal remain non-stationary, which is insufficient to enable comparisons between different acquisitions with different statistical characteristics. In this work, we propose a statistical characterization of noise in reconstructed CT scans that leads to a versatile statistical model that effectively characterizes different doses, reconstruction kernels, and devices. The statistical model is generalized to deal with the partial volume effect via a localized mixture model that also describes the non-stationarity of noise. Finally, we propose a stabilization scheme to achieve stationary variance. The validation of the proposed methodology was performed with a physical phantom and clinical CT scans acquired with different configurations (kernels, doses, algorithms including iterative reconstruction). The results confirmed its suitability to enable comparisons with different doses, and acquisition protocols.

Published
2016

12. Right ventricle segmentation from cardiac MRI: a collation study

Author

Oskar Maier, Haiyan Wang, Daniel Rueckert, Jérôme Caudron, Wenzhe Shi, Ismail Ben Ayed, Chun Wei Peng, Caroline Petitjean, Jean-Nicolas Dacher, Sebastien Ourselin, Daniel Jimenez-Carretero, Ching-Wei Wang, Su Ruan, Guray Erus, Christos Davatzikos, Maria A. Zuluaga, Yangming Ou, Maria J. Ledesma-Carbayo, Hsiang Chou Chen, Damien Grosgeorge, Andres Santos, Terry M. Peters, Jimit Doshi, Wenjia Bai, Cyrus M. S. Nambakhsh, Martin Rajchl, Jing Yuan, M. Jorge Cardoso, Nicholas S. Peters, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Centre for Medical Image Computing (CMIC), University College of London [London] (UCL), Department of Computing [London], Biomedical Image Analysis Group [London] (BioMedIA), Imperial College London-Imperial College London, Service d'imagerie médicale [CHU Rouen], Hôpital Charles Nicolle [Rouen]-CHU Rouen, Normandie Université (NU), GE Healthcare, Graduate institute of biomedical engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, Biomedical Image Technologies, Biomedical Research Center (CIBER-BBN), Universidad Politécnica de Madrid (UPM), Section for Biomedical Image Analysis (SBIA), Perelman School of Medicine, University of Pennsylvania [Philadelphia]-University of Pennsylvania [Philadelphia], Department of Electrical & Computer Engineering, University of Western Ontario, London, ONT. N6A 5B9, Canada, Department of Electrical and Computer Engineering, University of Western Ontario (UWO)-University of Western Ontario (UWO), National Heart and Lung Institute, Imperial College London, BHF (RG/10/11/28457), NIHR Biomedical Research Centre, EPSRC (EP/H046410/1, EP/J020990/1, EP/K005278), MRC (MR/J01107X/1), NIHR Biomedical Research Unit (Dementia) at UCL, National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative), National Science Council of Taiwan (NSC101-2628-E-011-006-MY3), Spanish Ministry of Science and Innovation through CDTI CENIT (AMIT), Comunidad de Madrid (ARTEMIS S2009/DPI-1802), European Funds (FEDER) [TEC2010-21619-004-03, TEC2011-28972-C02-02], EU-FP7 project VPH-DARE@IT (FP7-ICT-2011-9-601055), European Project: 269300,EC:FP7:PEOPLE,FP7-PEOPLE-2010-IRSES,TAHITI(2012), Equipe Quantification en Imagerie Fonctionnelle (QuantIF-LITIS), Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH), Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Seccion Fisiologia y Nutricion, Universidad de la República [Montevideo] (UCUR), Service d'imagerie médicale [Rouen], BRU-UNIDE, Instituto Superior Técnico, Universidade Técnica de Lisboa (IST), National Tsin Hua University, Department of Electrical Engineering, Laboratoire Angevin de Recherche en Mathématiques (LAREMA), Université d'Angers (UA)-Centre National de la Recherche Scientifique (CNRS), Department of Biology [Utah], University of Utah, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL), Université Catholique de Louvain (UCL), Joint Research Laboratory on Spatial Informations, The Hong Kong Polytechnic University [Hong Kong] (POLYU), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), University of Strathclyde [Glasgow], Hôpital Charles Nicolle [Rouen], CHU Rouen, Normandie Université (NU)-Normandie Université (NU)-CHU Rouen, and University of Pennsylvania-University of Pennsylvania

Subjects
Male, Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Segmentation challenge, [SDV]Life Sciences [q-bio], Heart Ventricles, Magnetic Resonance Imaging, Cine, Health Informatics, Dice, Tracing, Sensitivity and Specificity, Pattern Recognition, Automated, Ventricular Dysfunction, Left, Imaging, Three-Dimensional, Segmentation method evaluation, Image Interpretation, Computer-Assisted, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Radiology, Nuclear Medicine and imaging, Computer vision, Segmentation, [INFO]Computer Science [cs], Cardiac MRI, Collation study, Radiological and Ultrasound Technology, medicine.diagnostic_test, business.industry, Reproducibility of Results, Magnetic resonance imaging, Middle Aged, Image Enhancement, Computer Graphics and Computer-Aided Design, Right ventricle segmentation, Task (computing), Hausdorff distance, medicine.anatomical_structure, Ventricle, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Subtraction Technique, Metric (mathematics), Female, Computer Vision and Pattern Recognition, Artificial intelligence, business, Algorithms
Abstract

International audience; Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI'12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/).

Published
2014
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13. Organ-focused mutual information for nonrigid multimodal registration of liver CT and Gd-EOB-DTPA-enhanced MRI

Author

Jan Kybic, Javier Pascau, Maria J. Ledesma-Carbayo, Jose M. Tellado, Manuel Desco, Gert Wollny, Enrique Ramon, Daniel Jimenez-Carretero, Laura Fernandez-de-Manuel, and Andres Santos

Subjects
Gadolinium DTPA, Channel (digital image), Medicina, Gd-EOB-DTPA, Image registration, Contrast Media, Health Informatics, Computed tomography, 02 engineering and technology, Multimodal Imaging, Sensitivity and Specificity, 030218 nuclear medicine & medical imaging, Liver ct, Pattern Recognition, Automated, 03 medical and health sciences, 0302 clinical medicine, Image Interpretation, Computer-Assisted, 0202 electrical engineering, electronic engineering, information engineering, Medicine, Humans, Radiology, Nuclear Medicine and imaging, Computer vision, Image warping, Telecomunicaciones, Radiological and Ultrasound Technology, medicine.diagnostic_test, business.industry, Liver Neoplasms, Reproducibility of Results, Magnetic resonance imaging, Mutual information, Image Enhancement, Computer Graphics and Computer-Aided Design, Magnetic Resonance Imaging, Subtraction Technique, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, Tomography, X-Ray Computed, Algorithms
Abstract

Accurate detection of liver lesions is of great importance in hepatic surgery planning. Recent studies have shown that the detection rate of liver lesions is significantly higher in gadoxetic acid-enhanced magnetic resonance imaging (Gd–EOB–DTPA-enhanced MRI) than in contrast-enhanced portal-phase computed tomography (CT); however, the latter remains essential because of its high specificity, good performance in estimating liver volumes and better vessel visibility. To characterize liver lesions using both the above image modalities, we propose a multimodal nonrigid registration framework using organ-focused mutual information (OF-MI). This proposal tries to improve mutual information (MI) based registration by adding spatial information, benefiting from the availability of expert liver segmentation in clinical protocols. The incorporation of an additional information channel containing liver segmentation information was studied. A dataset of real clinical images and simulated images was used in the validation process. A Gd–EOB–DTPA-enhanced MRI simulation framework is presented. To evaluate results, warping index errors were calculated for the simulated data, and landmark-based and surface-based errors were calculated for the real data. An improvement of the registration accuracy for OF-MI as compared with MI was found for both simulated and real datasets. Statistical significance of the difference was tested and confirmed in the simulated dataset (p < 0.01).

Published
2012

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