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Understanding, Modeling and Detecting Brain Tumors: Graphical Models and Concurrent Segmentation/Registration Methods Sarah Parisot

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Understanding, Modeling and Detecting Brain Tumors: Graphical Models and Concurrent Segmentation/Registration Methods Sarah Parisot Understanding, Modeling and Detecting Brain Tumors: Graphical Models and Concurrent Segmentation/Registration methods Sarah Parisot To cite this version: Sarah Parisot. Understanding, Modeling and Detecting Brain Tumors: Graphical Models and Con- current Segmentation/Registration methods. Engineering Sciences [physics]. Ecole Centrale Paris, 2013. English. tel-00944541v2 HAL Id: tel-00944541 https://tel.archives-ouvertes.fr/tel-00944541v2 Submitted on 24 Feb 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. ECOLE CENTRALE PARIS PHDTHESIS to obtain the title of Doctor of Ecole Centrale Paris Specialty : APPLIED MATHEMATICS Defended by Sarah PARISOT Understanding, Modeling and Detecting Brain Tumors: Graphical Models and Concurrent Segmentation/Registration Methods prepared at Ecole Centrale Paris, Center for Visual Computing defended on November 18, 2013 Jury : Reviewers : Christos DAVATZIKOS - University of Pennsylvania Alison NOBLE - University of Oxford Advisor : Nikos PARAGIOS - Ecole Centrale Paris Examiners : Nicholas AYACHE - INRIA - Sophia-Antipolis Stéphane CHEMOUNY - Intrasense SAS Emmanuel MANDONNET - Hôpital de Lariboisière William WELLS III - Harvard Medical School 2 Acknowledgments First of all, I would like to express my sincere gratitude towards my advisor Nikos Paragios for his continuous support and advice during my whole PhD. It has been a long journey, and I certainly wouldn’t be where I am now if it wasn’t for him. Then, I would like to thank Intrasense and more specifically its CEO Stéphane Chemouny, as well as Hugues Duffau for trusting me with this project and for the freedom I was given during this thesis. I will always remember the incredible opportunity I was given to observe a brain surgery at the beginning of my thesis, which was a truly fascinating experience. Second, I would like to express my deepest gratitude towards all the members of my thesis committee: the reviewers Alison Noble and Christos Davatzikos, the chairman Nicholas Ayache and the examiners William Wells III and Emmanuel Mandonnet, for taking time to read and evalu- ate my work and their insightful comments and questions. I would like to thank William Wells for hosting me in the Surgical Planning Laboratory and the three great months I spent there. I was an incredibly interesting and fruitful experience that completely renewed my motivation for my work. I would also like to thank all the members of the lab for their warm welcome and the fun times we spent together. I thank Amélie Darlix for the enormous work she carried out constructing the DLGG database and her help and advice on the clinical aspect of my work. I would like to acknowledge Bo (aka Crazy Bobo) who has been a wonderful (the best!) col- league and friend throughout those years. Thank you for all the experiences we shared together, I am very glad that I had you by my side during the tough (such as our first MICCAI deadline) and the happy times. I would also like to thank Blandine for being such a great friend, I am thankful that we started our crazy PhD adventure almost simultaneously. To a larger extend, I thank all my great labmates for making this lab such a better place (and for eating my million cakes): Katerina, Aris, Loïc, Olivier, Chaohui, Panos, Pierre-Yves, Nicolas, Vivien, Sylvain, Fabrice, Haithem, the Stavroses, Nacho, Enzo, Eduard, Puneet, Krishna, Siddharta, Dimitris, Grigoris, Evgenios, Stefan, Mateuzs, Matthew, Iasonas and Pawan. Thank you for the countless times you have helped me with work, the Chinese and belated Greek lessons, the tea/coffee breaks, the sweets from all over the world, the dinner parties... I would like to thank Sylvie and Annie from MAS laboratory, as well as Carine and Natalia from CVN for their invaluable help and support as well as the great discussions we had together. I would also like to acknowledge my former ECP teachers Moncef Stambouli and Florence Mayo- 3 4 Quenette with whom I was always happy to cross path and have a chat. Outside the lab, I would like to thank my dear friends who have supported me during all those years and made sure I had a social life, even during the dreaded thesis writing. Life would have been much harder without my precious high school friends Juliette (one of my tiniest yet greatest friend), Nicolas (my photo wander partner), Olivier (my favorite Swiss) and Pauline (when we manage to see each other); my Intrasense penpal Guillaume; and of course, my wonderful ECP family “Centraliénés” Alexandra, Aline, Amélie, Anne-Gabrielle, Céline, Eric, Gabrielle, Guillemette, Philippe and Victor. Finally, I dedicate this thesis to my parents and sisters, whose unconditional love and support got me through the hardest moments and with whom I was glad to share the happy ones. I feel incredibly lucky to have such a great family and I love you all very deeply. Abstract The main objective of this thesis is the automatic modeling, understanding and segmentation of diffusively infiltrative tumors known as Diffuse Low-Grade Gliomas. Two approaches exploiting anatomical and spatial prior knowledge have been proposed. We first present the construction of a tumor specific probabilistic atlas describing the tumors’ preferential locations in the brain. The proposed atlas constitutes an excellent tool for the study of the mechanisms behind the genesis of the tumors and provides strong spatial cues on where they are expected to appear. The latter characteristic is exploited in a Markov Random Field based segmentation method where the atlas guides the segmentation process as well as characterizes the tumor’s preferential location. Second, we introduce a concurrent tumor segmentation and registration with missing corre- spondences method. The anatomical knowledge introduced by the registration process increases the segmentation quality, while progressively acknowledging the presence of the tumor ensures that the registration is not violated by the missing correspondences without the introduction of a bias. The method is designed as a hierarchical grid-based Markov Random Field model where the segmentation and registration parameters are estimated simultaneously on the grid’s control point. The last contribution of this thesis is an uncertainty-driven adaptive sampling approach for such grid-based models in order to ensure precision and accuracy while maintaining robustness and computational efficiency. The potentials of both methods have been demonstrated on a large data-set of heterogeneous -in appearance, size and shape- Diffuse Low-Grade Gliomas. The proposed methods go beyond the scope of the presented clinical context due to their strong modularity and could easily be adapted to other clinical or computer vision problems. Keywords: Markov Random Fields, Registration, Segmentation, Brain Atlas, Diffuse Low-Grade Gliomas 5 6 Résumé L’objectif principal de cette thèse est la modélisation, compréhension et segmentation automatique de tumeurs diffuses et infiltrantes appelées Gliomes Diffus de Bas Grade. Deux approches ex- ploitant des connaissances a priori de l’ordre spatial et anatomique ont été proposées. Dans un premier temps, la construction d’un atlas probabiliste qui illustre les positions préférentielles des tumeurs dans le cerveau est présentée. Cet atlas représente un excellent outil pour l’étude des mécanismes associés à la genèse des tumeurs et fournit des indications sur la position probable des tumeurs. Cette information est exploitée dans une méthode de segmentation basée sur des champs de Markov aléatoires, dans laquelle l’atlas guide la segmentation et caractérise la position préférentielle de la tumeur. Dans un second temps, nous présentons une méthode pour la segmentation de tumeur et le recalage avec absence de correspondances simultanés. Le recalage introduit des informations anatomiques qui améliorent les résultats de segmentation tandis que la détection progressive de la tumeur permet de surmonter l’absence de correspondances sans l’introduction d’un à priori. La méthode est modélisée comme un champ de Markov aléatoire hiérarchique et à base de grille sur laquelle les paramètres de segmentation et recalage sont estimés simultanément. Notre dernière contribution est une méthode d’échantillonnage adaptatif guidé par les incertitudes pour de tels modèles discrets. Ceci permet d’avoir une grande précision tout en maintenant la robustesse et ra- pidité de la méthode. Le potentiel des deux méthodes est démontré sur de grandes bases de données de gliomes diffus de bas grade hétérogènes (en taille, forme et apparence). De par leur modularité, les méthodes proposées ne se limitent pas au contexte clinique présenté et pourraient facilement être adaptées à d’autres problèmes cliniques ou de vision par ordinateur. Keywords: Champs de Markov Aléatoires, Recalage, Segmentation, Atlas du Cerveau, Gliomes Diffus de Bas Grade 7 8 Contents 1 Introduction 15 1.1 Context and Motivation ................................ 15 1.1.1 Diffuse Low-Grade Gliomas: Introduction .................. 16 1.1.2 Therapy and Functional Reshaping ....................
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