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1. Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation

Author

Sadikine, Amine, Badic, Bogdan, Tasu, Jean-Pierre, Noblet, Vincent, Visvikis, Dimitris, and Conze, Pierre-Henri

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The extraction of blood vessels has recently experienced a widespread interest in medical image analysis. Automatic vessel segmentation is highly desirable to guide clinicians in computer-assisted diagnosis, therapy or surgical planning. Despite a good ability to extract large anatomical structures, the capacity of U-Net inspired architectures to automatically delineate vascular systems remains a major issue, especially given the scarcity of existing datasets. In this paper, we present a novel approach that integrates into deep segmentation shape priors from a Semi-Overcomplete Convolutional Auto-Encoder (S-OCAE) embedding. Compared to standard Convolutional Auto-Encoders (CAE), it exploits an over-complete branch that projects data onto higher dimensions to better characterize tiny structures. Experiments on retinal and liver vessel extraction, respectively performed on publicly-available DRIVE and 3D-IRCADb datasets, highlight the effectiveness of our method compared to U-Net trained without and with shape priors from a traditional CAE., Comment: 5 pages, 4 figures, conference

Published
2024
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2. Deep vessel segmentation with joint multi-prior encoding

Author

Sadikine, Amine, Badic, Bogdan, Ferrante, Enzo, Noblet, Vincent, Ballet, Pascal, Visvikis, Dimitris, and Conze, Pierre-Henri

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The precise delineation of blood vessels in medical images is critical for many clinical applications, including pathology detection and surgical planning. However, fully-automated vascular segmentation is challenging because of the variability in shape, size, and topology. Manual segmentation remains the gold standard but is time-consuming, subjective, and impractical for large-scale studies. Hence, there is a need for automatic and reliable segmentation methods that can accurately detect blood vessels from medical images. The integration of shape and topological priors into vessel segmentation models has been shown to improve segmentation accuracy by offering contextual information about the shape of the blood vessels and their spatial relationships within the vascular tree. To further improve anatomical consistency, we propose a new joint prior encoding mechanism which incorporates both shape and topology in a single latent space. The effectiveness of our method is demonstrated on the publicly available 3D-IRCADb dataset. More globally, the proposed approach holds promise in overcoming the challenges associated with automatic vessel delineation and has the potential to advance the field of deep priors encoding., Comment: 5 pages, 3 figures, conference

Published
2024
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3. Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation

Author

Sadikine, Amine, Badic, Bogdan, Tasu, Jean-Pierre, Noblet, Vincent, Ballet, Pascal, Visvikis, Dimitris, and Conze, Pierre-Henri

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-independent Couinaud segments. In this respect, an automated liver blood vessel extraction is widely summoned. Despite the significant growth in performance of semantic segmentation methodologies, preserving the complex multi-scale geometry of main vessels and ramifications remains a major challenge. This paper provides a new deep supervised approach for vessel segmentation, with a strong focus on representations arising from the different scales inherent to the vascular tree geometry. In particular, we propose a new clustering technique to decompose the tree into various scale levels, from tiny to large vessels. Then, we extend standard 3D UNet to multi-task learning by incorporating scale-specific auxiliary tasks and contrastive learning to encourage the discrimination between scales in the shared representation. Promising results, depicted in several evaluation metrics, are revealed on the public 3D-IRCADb dataset., Comment: 5 pages, 5 figures, conference

Published
2024
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4. Impact of Initialization on Intra-subject Pediatric Brain MR Image Registration: A Comparative Analysis between SyN ANTs and Deep Learning-Based Approaches

Author

Dimitrijevic, Andjela, Noblet, Vincent, and De Leener, Benjamin

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Neurons and Cognition
Abstract

This study evaluates the performance of conventional SyN ANTs and learning-based registration methods in the context of pediatric neuroimaging, specifically focusing on intrasubject deformable registration. The comparison involves three approaches: without (NR), with rigid (RR), and with rigid and affine (RAR) initializations. In addition to initialization, performances are evaluated in terms of accuracy, speed, and the impact of age intervals and sex per pair. Data consists of the publicly available MRI scans from the Calgary Preschool dataset, which includes 63 children aged 2-7 years, allowing for 431 registration pairs. We implemented the unsupervised DL framework with a U-Net architecture using DeepReg and it was 5-fold cross-validated. Evaluation includes Dice scores for tissue segmentation from 18 smaller regions obtained by SynthSeg, analysis of log Jacobian determinants, and registration pro-rated training and inference times. Learning-based approaches, with or without linear initializations, exhibit slight superiority over SyN ANTs in terms of Dice scores. Indeed, DL-based implementations with RR and RAR initializations significantly outperform SyN ANTs. Both SyN ANTs and DL-based registration involve parameter optimization, but the choice between these methods depends on the scale of registration: network-based for broader coverage or SyN ANTs for specific structures. Both methods face challenges with larger age intervals due to greater growth changes. The main takeaway is that while DL-based methods show promise with faster and more accurate registrations, SyN ANTs remains robust and generalizable without the need for extensive training, highlighting the importance of method selection based on specific registration needs in the pediatric context. Our code is available at https://github.com/neuropoly/pediatric-DL-registration, Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:013

Published
2024
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10. Cerebral perfusion using ASL in patients with COVID-19 and neurological manifestations: A retrospective multicenter observational study

Author

Bonneville, Fabrice, Adam, Gilles, Martin-Blondel, Guillaume, Pariente, Jérémie, Geeraerts, Thomas, Oesterlé, Hélène, Bolognini, Federico, Messie, Julien, Hmeydia, Ghazi, Benzakoun, Joseph, Oppenheim, Catherine, Constans, Jean-Marc, Metanbou, Serge, Heintz, Adrien, Bapst, Blanche, Megdiche, Imen, Jager, Lavinia, Nesser, Patrick, Mba, Yannick Talla, Tourdias, Thomas, Coutureau, Juliette, Hemmert, Céline, Feuerstein, Philippe, Sebag, Nathan, Carre, Sophie, Alleg, Manel, Lecocq, Claire, Schmitt, Emmanuel, Anxionnat, René, Zhu, François, Forestier, Géraud, Rouchaud, Aymeric, Comby, Pierre-Olivier, Ricolfi, Frederic, Thouant, Pierre, Grand, Sylvie, Krainik, Alexandre, de Beaurepaire, Isaure, Bornet, Grégoire, Lacalm, Audrey, Miailhes, Patrick, Pique, Julie, Boutet, Claire, Fabre, Xavier, Claise, Béatrice, Mirafzal, Sonia, Calvet, Laure, Desal, Hubert, Berge, Jérome, Boulouis, Grégoire, Kazemi, Apolline, Pyatigorskaya, Nadya, Lecler, Augustin, Saleme, Suzana, Edjlali-Goujon, Myriam, Kerleroux, Basile, Brisset, Jean-Christophe, Chenaf, Samir, Ardellier, François-Daniel, Baloglu, Seyyid, Sokolska, Magdalena, Noblet, Vincent, Lersy, François, Collange, Olivier, Ferré, Jean-Christophe, Maamar, Adel, Carsin-Nicol, Béatrice, Helms, Julie, Schenck, Maleka, Khalil, Antoine, Gaudemer, Augustin, Caillard, Sophie, Pottecher, Julien, Lefèbvre, Nicolas, Zorn, Pierre-Emmanuel, Matthieu, Muriel, Brisset, Jean Christophe, Boulay, Clotilde, Mutschler, Véronique, Hansmann, Yves, Mertes, Paul-Michel, Schneider, Francis, Fafi-Kremer, Samira, Ohana, Mickael, Meziani, Ferhat, Meyer, Nicolas, Yousry, Tarek, Anheim, Mathieu, Cotton, François, Jäger, Hans Rolf, and Kremer, Stéphane

Published
2023
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11. Deep Learning-Based Longitudinal Intra-subject Registration of Pediatric Brain MR Images

Author

Dimitrijevic, Andjela, Noblet, Vincent, De Leener, Benjamin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hering, Alessa, editor, Schnabel, Julia, editor, Zhang, Miaomiao, editor, Ferrante, Enzo, editor, Heinrich, Mattias, editor, and Rueckert, Daniel, editor

Published
2022
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22. Controllability of Functional and Structural Brain Networks.

Author

Moradi Amani, Ali, Tahmassebi, Amirhessam, Stadlbauer, Andreas, Meyer-Baese, Uwe, Noblet, Vincent, Blanc, Frederic, Malberg, Hagen, Meyer-Baese, Anke, and Mariño, Inés P.

Subjects
LARGE-scale brain networks, NEURAL circuitry, LAPLACIAN matrices, NEURODEGENERATION, GRAPH theory, CONTROLLABILITY in systems engineering
Abstract

Normal and aberrant cognitive functions are the result of the dynamic interplay between large‐scale neural circuits. Describing the nature of these interactions has been a challenging task yet important for neurodegenerative disease evolution. Fusing modern dynamic graph network theory techniques and control theory applied on complex brain networks creates a new framework for neurodegenerative disease research by determining disease evolution at the subject level, facilitating a predictive treatment response and revealing key mechanisms responsible for disease alterations. It has been shown that two types of controllability—the average and the modal controllability—are relevant for the mechanistic explanation of how the brain navigates between cognitive states. The average controllability favors highly connected areas which move the brain to easily reachable states, while the modal controllability favors weakly connected areas representative for difficult‐to‐reach states. We propose two different techniques to achieve these two types of controllability: a centrality measure based on a sensitivity analysis of the Laplacian matrix is employed to determine the average controllability, while graph distances form the basis of the modal controllability. The concepts of "choosing the best driver set" and "graph distances" are applied to measure the average controllability and the modal controllability, respectively. Based on these new techniques, we obtain important disease descriptors that visualize alterations in the disease trajectory by revealing densely connected hubs or sparser areas. Our results suggest that these two techniques can accurately describe the different node roles in controlling trajectories of brain networks. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Derivative Half Gaussian Kernels and Shock Filter

Author

Magnier, Baptiste, Noblet, Vincent, Voisin, Adrien, Legouestre, Dylan, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Blanc-Talon, Jacques, editor, Helbert, David, editor, Philips, Wilfried, editor, Popescu, Dan, editor, and Scheunders, Paul, editor

Published
2018
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24. Robust Supervoxel Matching Combining Mid-Level Spectral and Context-Rich Features

Author

Tilquin, Florian, Conze, Pierre-Henri, Pessaux, Patrick, Lamard, Mathieu, Quellec, Gwenolé, Noblet, Vincent, Heitz, Fabrice, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Bai, Wenjia, editor, Sanroma, Gerard, editor, Wu, Guorong, editor, Munsell, Brent C., editor, Zhan, Yiqiang, editor, and Coupé, Pierrick, editor

Published
2018
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34. Detecting and quantifying spatial misalignment between longitudinal kilovoltage computed tomography (kVCT) scans of the head and neck by using convolutional neural networks (CNNs)

Author

Lallement, Alex, Noblet, Vincent, Antoni, Delphine, Meyer, Philippe, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Institut Pluridisciplinaire Hubert Curien (IPHC), and Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Biomaterials, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Biomedical Engineering, Biophysics, Health Informatics, Bioengineering, Information Systems
Abstract

BACKGROUND: Adaptive radiotherapy (ART) aims to address anatomical modifications appearing during the treatment of patients by modifying the planning treatment according to the daily positioning image. Clinical implementation of ART relies on the quality of the deformable image registration (DIR) algorithms included in the ART workflow. To translate ART into clinical practice, automatic DIR assessment is needed. OBJECTIVE: This article aims to estimate spatial misalignment between two head and neck kilovoltage computed tomography (kVCT) images by using two convolutional neural networks (CNNs). METHODS: The first CNN quantifies misalignments between 0 mm and 15 mm and the second CNN detects and classifies misalignments into two classes (poor alignment and good alignment). Both networks take pairs of patches of 33x33x33 mm3 as inputs and use only the image intensity information. The training dataset was built by deforming kVCT images with basis splines (B-splines) to simulate DIR error maps. The test dataset was built using 2500 landmarks, consisting of hard and soft landmark tissues annotated by 6 clinicians at 10 locations. RESULTS: The quantification CNN reaches a mean error of 1.26 mm (± 1.75 mm) on the landmark set which, depending on the location, has annotation errors between 1 mm and 2 mm. The errors obtained for the quantification network fit the computed interoperator error. The classification network achieves an overall accuracy of 79.32%, and although the classification network overdetects poor alignments, it performs well (i.e., it achieves a rate of 90.4%) in detecting poor alignments when given one. CONCLUSION: The performances of the networks indicate the feasibility of using CNNs for an agnostic and generic approach to misalignment quantification and detection.

Published
2023
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38. Semi-automatic Liver Tumor Segmentation in Dynamic Contrast-Enhanced CT Scans Using Random Forests and Supervoxels

Author

Conze, Pierre-Henri, Rousseau, François, Noblet, Vincent, Heitz, Fabrice, Memeo, Riccardo, Pessaux, Patrick, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Zhou, Luping, editor, Wang, Li, editor, Wang, Qian, editor, and Shi, Yinghuan, editor

Published
2015
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42. White matter tracts and executive functions: a review of causal and correlation evidence.

Author

Ribeiro, Monica, Yordanova, Yordanka Nikolova, Noblet, Vincent, Herbet, Guillaume, and Ricard, Damien

Subjects
EXECUTIVE function, WHITE matter (Nerve tissue), DIFFUSION tensor imaging, BRAIN tumors, CORPUS callosum, RESPONSE inhibition
Abstract

Executive functions are high-level cognitive processes involving abilities such as working memory/updating, set-shifting and inhibition. These complex cognitive functions are enabled by interactions among widely distributed cognitive networks, supported by white matter tracts. Executive impairment is frequent in neurological conditions affecting white matter; however, whether specific tracts are crucial for normal executive functions is unclear. We review causal and correlation evidence from studies that used direct electrical stimulation during awake surgery for gliomas, voxel-based and tract-based lesion-symptom mapping, and diffusion tensor imaging to explore associations between the integrity of white matter tracts and executive functions in healthy and impaired adults. The corpus callosum was consistently associated with all executive processes, notably its anterior segments. Both causal and correlation evidence showed prominent support of the superior longitudinal fasciculus to executive functions, notably to working memory. More specifically, strong evidence suggested that the second branch of the superior longitudinal fasciculus is crucial for all executive functions, especially for flexibility. Global results showed left lateralization for verbal tasks and right lateralization for executive tasks with visual demands. The frontal aslant tract potentially supports executive functions, however, additional evidence is needed to clarify whether its involvement in executive tasks goes beyond the control of language. Converging evidence indicates that a right-lateralized network of tracts connecting cortical and subcortical grey matter regions supports the performance of tasks assessing response inhibition, some suggesting a role for the right anterior thalamic radiation. Finally, correlation evidence suggests a role for the cingulum bundle in executive functions, especially in tasks assessing inhibition. We discuss these findings in light of current knowledge about the functional role of these tracts, descriptions of the brain networks supporting executive functions and clinical implications for individuals with brain tumours. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Longitudinal Change Detection: Inference on the Diffusion Tensor Along White-Matter Pathways

Author

Grigis, Antoine, Noblet, Vincent, Blanc, Fréderic, Heitz, Fabrice, de Seze, Jérome, Armspach, Jean-Paul, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Fichtinger, Gabor, editor, Martel, Anne, editor, and Peters, Terry, editor

Published
2011
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45. Cerebral perfusion using ASL in patients with COVID-19 and neurological manifestations: A retrospective multicenter observational study

Author

Ardellier, François-Daniel, primary, Baloglu, Seyyid, additional, Sokolska, Magdalena, additional, Noblet, Vincent, additional, Lersy, François, additional, Collange, Olivier, additional, Ferré, Jean-Christophe, additional, Maamar, Adel, additional, Carsin-Nicol, Béatrice, additional, Helms, Julie, additional, Schenck, Maleka, additional, Khalil, Antoine, additional, Gaudemer, Augustin, additional, Caillard, Sophie, additional, Pottecher, Julien, additional, Lefèbvre, Nicolas, additional, Zorn, Pierre-Emmanuel, additional, Matthieu, Muriel, additional, Brisset, Jean Christophe, additional, Boulay, Clotilde, additional, Mutschler, Véronique, additional, Hansmann, Yves, additional, Mertes, Paul-Michel, additional, Schneider, Francis, additional, Fafi-Kremer, Samira, additional, Ohana, Mickael, additional, Meziani, Ferhat, additional, Meyer, Nicolas, additional, Yousry, Tarek, additional, Anheim, Mathieu, additional, Cotton, François, additional, Jäger, Hans Rolf, additional, Kremer, Stéphane, additional, Bonneville, Fabrice, additional, Adam, Gilles, additional, Martin-Blondel, Guillaume, additional, Pariente, Jérémie, additional, Geeraerts, Thomas, additional, Oesterlé, Hélène, additional, Bolognini, Federico, additional, Messie, Julien, additional, Hmeydia, Ghazi, additional, Benzakoun, Joseph, additional, Oppenheim, Catherine, additional, Constans, Jean-Marc, additional, Metanbou, Serge, additional, Heintz, Adrien, additional, Bapst, Blanche, additional, Megdiche, Imen, additional, Jager, Lavinia, additional, Nesser, Patrick, additional, Mba, Yannick Talla, additional, Tourdias, Thomas, additional, Coutureau, Juliette, additional, Hemmert, Céline, additional, Feuerstein, Philippe, additional, Sebag, Nathan, additional, Carre, Sophie, additional, Alleg, Manel, additional, Lecocq, Claire, additional, Schmitt, Emmanuel, additional, Anxionnat, René, additional, Zhu, François, additional, Forestier, Géraud, additional, Rouchaud, Aymeric, additional, Comby, Pierre-Olivier, additional, Ricolfi, Frederic, additional, Thouant, Pierre, additional, Grand, Sylvie, additional, Krainik, Alexandre, additional, de Beaurepaire, Isaure, additional, Bornet, Grégoire, additional, Lacalm, Audrey, additional, Miailhes, Patrick, additional, Pique, Julie, additional, Boutet, Claire, additional, Fabre, Xavier, additional, Claise, Béatrice, additional, Mirafzal, Sonia, additional, Calvet, Laure, additional, Desal, Hubert, additional, Berge, Jérome, additional, Boulouis, Grégoire, additional, Kazemi, Apolline, additional, Pyatigorskaya, Nadya, additional, Lecler, Augustin, additional, Saleme, Suzana, additional, Edjlali-Goujon, Myriam, additional, Kerleroux, Basile, additional, Brisset, Jean-Christophe, additional, and Chenaf, Samir, additional

Published
2023
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46. Change Detection in Diffusion MRI Using Multivariate Statistical Testing on Tensors

Author

Grigis, Antoine, Noblet, Vincent, Renard, Félix, Heitz, Fabrice, Armspach, Jean-Paul, Rumbach, Lucien, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Jiang, Tianzi, editor, Navab, Nassir, editor, Pluim, Josien P. W., editor, and Viergever, Max A., editor

Published
2010
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47. Use of Simulated Atrophy for Performance Analysis of Brain Atrophy Estimation Approaches

Author

Sharma, Swati, Noblet, Vincent, Rousseau, François, Heitz, Fabrice, Rumbach, Lucien, Armspach, Jean-Paul, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yang, Guang-Zhong, editor, Hawkes, David, editor, Rueckert, Daniel, editor, Noble, Alison, editor, and Taylor, Chris, editor

Published
2009
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48. Statistical Detection of Longitudinal Changes between Apparent Diffusion Coefficient Images: Application to Multiple Sclerosis

Author

Boisgontier, Hervé, Noblet, Vincent, Renard, Félix, Heitz, Fabrice, Rumbach, Lucien, Armspach, Jean-Paul, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Yang, Guang-Zhong, editor, Hawkes, David, editor, Rueckert, Daniel, editor, Noble, Alison, editor, and Taylor, Chris, editor

Published
2009
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