A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Advanced Algorithms for 3D Medical Image Data Fusion in Specific Medical Problems

Fúze obrazu je dnes jednou z nejběžnějších avšak stále velmi diskutovanou oblastí v lékařském zobrazování a hraje důležitou roli ve všech oblastech lékařské péče jako je diagnóza, léčba a chirurgie. V této dizertační práci jsou představeny tři projekty, které jsou velmi úzce spojeny s oblastí fúze medicínských dat. První projekt pojednává o 3D CT subtrakční angiografii dolních končetin. V práci je využito kombinace kontrastních a nekontrastních dat pro získání kompletního cévního stromu. Druhý projekt se zabývá fúzí DTI a T1 váhovaných MRI dat mozku. Cílem tohoto projektu je zkombinovat stukturální a funkční informace, které umožňují zlepšit znalosti konektivity v mozkové tkáni. Třetí projekt se zabývá metastázemi v CT časových datech páteře. Tento projekt je zaměřen na studium vývoje metastáz uvnitř obratlů ve fúzované časové řadě snímků. Tato dizertační práce představuje novou metodologii pro klasifikaci těchto metastáz. Všechny projekty zmíněné v této dizertační práci byly řešeny v rámci pracovní skupiny zabývající se analýzou lékařských dat, kterou vedl pan Prof. Jiří Jan. Tato dizertační práce obsahuje registrační část prvního a klasifikační část třetího projektu. Druhý projekt je představen kompletně. Další část prvního a třetího projektu, obsahující specifické předzpracování dat, jsou obsaženy v disertační práci mého kolegy Ing. Romana Petera.Image fusion is one of today´s most common and still challenging tasks in medical imaging and it plays crucial role in all areas of medical care such as diagnosis, treatment and surgery. Three projects crucially dependent on image fusion are introduced in this thesis. The first project deals with the 3D CT subtraction angiography of lower limbs. It combines pre-contrast and contrast enhanced data to extract the blood vessel tree. The second project fuses the DTI and T1-weighted MRI brain data. The aim of this project is to combine the brain structural and functional information that purvey improved knowledge about intrinsic brain connectivity. The third project deals with the time series of CT spine data where the metastases occur. In this project the progression of metastases within the vertebrae is studied based on fusion of the successive elements of the image series. This thesis introduces new methodology of classifying metastatic tissue. All the projects mentioned in this thesis have been solved by the medical image analysis group led by Prof. Jiří Jan. This dissertation concerns primarily the registration part of the first project and the classification part of the third project. The second project is described completely. The other parts of the first and third project, including the specific preprocessing of the data, are introduced in detail in the dissertation thesis of my colleague Roman Peter, M.Sc.

Fusion and Analysis of Multidimensional Medical Image Data

Analýza medicínských obrazů je předmětem základního výzkumu již řadu let. Za tu dobu bylo v této oblasti publikováno mnoho výzkumných prací zabývajících se dílčími částmi jako je rekonstrukce obrazů, restaurace, segmentace, klasifikace, registrace (lícování) a fúze. Kromě obecného úvodu, pojednává tato disertační práce o dvou medicínsky orientovaných tématech, jež byla formulována ve spolupráci s Philips Netherland BV, divizí Philips Healthcare. První téma je zaměřeno na oblast zpracování obrazů subtrakční angiografie dolních končetin člověka získaných pomocí výpočetní X-Ray tomografie (CT). Subtrakční angiografie je obvykle využívaná při podezření na periferní cévní onemocnění (PAOD) nebo při akutním poškození dolních končetin jako jsou fraktury apod. Současné komerční metody nejsou dostatečně spolehlivé už v předzpracování, jako je například odstranění pacientského stolu, pokrývky, dlahy, apod. Spolehlivost a přesnost identifikace cév v subtrahovaných datech vedoucích v blízkosti kostí je v důsledku Partial Volume artefaktu rovněž nízká. Automatické odstranění kalcifikací nebo detekce malých cév doplňujících nezbytnou informaci o náhradním zásobení dolních končetin krví v případě přerušení hlavních zásobujících cév v současné době rovněž nesplňují kritéria pro plně automatické zpracování. Proto hlavním cílem týkající se tohoto tématu bylo vyvinout automatický systém, který by mohl současné nedostatky v CTSA vyšetření odstranit. Druhé téma je orientováno na identifikaci patologických změn na páteři člověka v CT obrazech se zaměřením na osteolytické a osteoblastické léze u jednotlivých obratlů. Tyto změny obvykle nastávají v důsledků postižení metastazujícím procesem rakovinového onemocnění. Pro detekci patologických změn je pak potřeba identifikace a segmentace jednotlivých obratlů. Přesnost analýzy jednotlivých lézí však závisí rovněž na správné identifikaci těla a zadních segmentů u jednotlivých obratlů a na segmentaci trabekulárního centra obratlů, tj. odstranění kortikální kosti. Během léčby mohou být pacienti skenováni vícekrát, obvykle s několika-mesíčním odstupem. Hodnocení případného vývoje již detekovaných patologických změn pak logicky vychází ze správné detekce patologií v jednotlivých obratlech korespondujících si v jednotlivých akvizicích. Jelikož jsou příslušné obratle v jednotlivých akvizicích obvykle na různé pozici, jejich fúze, vedoucí k analýze časového vývoje detekovaných patologií, je komplikovaná. Požadovaným výsledkem v tomto tématu je vytvoření komplexního systému pro detekci patologických změn v páteři, především osteoblastických a osteolytických lézí. Takový systém tedy musí umožnovat jak segmentaci jednotlivých obratlů, jejich automatické rozdělení na hlavní části a odstranění kortikální kosti, tak také detekci patologických změn a jejich hodnocení. Ačkoliv je tato disertační práce v obou výše zmíněných tématech primárně zaměřena na experimentální část zpracování medicínských obrazů, zabývá se všemi nezbytnými kroky, jako je předzpracování, registrace, dodatečné zpracování a hodnocení výsledků, vedoucími k možné aplikovatelnosti obou systému v klinické praxi. Jelikož oba systémy byly řešeny v rámci týmové spolupráce jako celek, u obou témat jsou pro některé konkrétní kroky uvedeny odkazy na doktorskou práci Miloše Malínského.Analysis of medical images has been subject of basic research for many years. Many research papers have been published in the field related to image analysis and focused on partial aspects such as reconstruction, restoration, segmentation and classification, registration (spatial alignment) and fusion. Besides the introduction of related general concepts used in medical image processing, this thesis deals with two specific medical problems formulated in cooperation with Philips Netherland BV, Philips Healthcare division. The first topic is focused on subtraction angiography in patients’ lower legs utilizing image data from X-Ray computed tomography (CT). CT subtraction angiography (CTSA) is typically used for indication of the Peripheral Artery Occlusive Disease (PAOD) and for examination of acute injuries of lower legs such as acute fractures, etc. Current methods in clinical praxis are not sufficient regarding the pre-processing such as masking of patient desk, cover, splint, etc. The subtraction of blood vessels adjacent to neighboring bones in lower legs is of low accuracy due to the Partial Volume artifact. Masking of calcifications and detection of tiny blood vessels complementing necessary information about the alternative blood supply in lower legs in case of obstruction in main arteries is also not reliable for fully automated process presently. Therefore, the main aim regarding this topic was to develop an automated framework that could overcome current shortcomings in CTSA examination. The second topic is oriented on the identification and evaluation of pathologic changes in human spine, focusing on osteolytic and osteoblastic lesions in individual vertebrae in CT images. Such changes occur typically as a consequence of metastasizing process of cancerous disease. For the detection of pathologic changes, an identification and segmentation of individual vertebrae is necessary. Moreover, the analysis of individual lesions in vertebrae depends also on correct identification of vertebral body and posterior segments of each vertebra, and on segmentation of their trabecular centers. Patients are typically examined more than once during their therapy. Then, the evaluation of possible tumorous progression is based on accurate detection of pathologies in individual vertebrae in the base-line and corresponding follow-up images. Since the corresponding vertebrae are in mutually different positions in the follow-up images, their fusion leading to the analysis of the lesion progression is complicated. The main aim regarding this topic is to develop a complex framework for detection of pathologic lesions on spine, with the main focus on osteoblastic and osteolystic lesions. Such system has to provide not only reliable segmentation of individual vertebrae and detection of their main regions but also the masking of their cortical bone, detection of their pathologic changes and their evaluation. Although this dissertation thesis is primarily oriented at the experimental part of medical image processing considering both the above mentioned topics, it deals with all necessary processing steps, i.e. preprocessing, image registration, post-processing and evaluation of results, leading to the future use of both frameworks in clinical practice. Since both frameworks were developed in a team, there are some chapters referring to the dissertation thesis of Milos Malinsky.

3D-3D Deformable Registration and Deep Learning Segmentation based Neck Diseases Analysis in MRI

Whiplash, cervical dystonia (CD), neck pain and work-related upper limb disorder (WRULD) are the most common diseases in the cervical region. Headaches, stiffness, sensory disturbance to the legs and arms, optical problems, aching in the back and shoulder, and auditory and visual problems are common symptoms seen in patients with these diseases. CD patients may also suffer tormenting spasticity in some neck muscles, with the symptoms possibly being acute and persisting for a long time, sometimes a lifetime. Whiplash-associated disorders (WADs) may occur due to sudden forward and backward movements of the head and neck occurring during a sporting activity or vehicle or domestic accident. These diseases affect private industries, insurance companies and governments, with the socio-economic costs significantly related to work absences, long-term sick leave, early disability and disability support pensions, health care expenses, reduced productivity and insurance claims. Therefore, diagnosing and treating neck-related diseases are important issues in clinical practice. The reason for these afflictions resulting from accident is the impairment of the cervical muscles which undergo atrophy or pseudo-hypertrophy due to fat infiltrating into them. These morphological changes have to be determined by identifying and quantifying their bio-markers before applying any medical intervention. Volumetric studies of neck muscles are reliable indicators of the proper treatments to apply. Radiation therapy, chemotherapy, injection of a toxin or surgery could be possible ways of treating these diseases. However, the dosages required should be precise because the neck region contains some sensitive organs, such as nerves, blood vessels and the trachea and spinal cord. Image registration and deep learning-based segmentation can help to determine appropriate treatments by analyzing the neck muscles. However, this is a challenging task for medical images due to complexities such as many muscles crossing multiple joints and attaching to many bones. Also, their shapes and sizes vary greatly across populations whereas their cross-sectional areas (CSAs) do not change in proportion to the heights and weights of individuals, with their sizes varying more significantly between males and females than ages. Therefore, the neck's anatomical variabilities are much greater than those of other parts of the human body. Some other challenges which make analyzing neck muscles very difficult are their compactness, similar gray-level appearances, intra-muscular fat, sliding due to cardiac and respiratory motions, false boundaries created by intramuscular fat, low resolution and contrast in medical images, noise, inhomogeneity and background clutter with the same composition and intensity. Furthermore, a patient's mode, position and neck movements during the capture of an image create variability. However, very little significant research work has been conducted on analyzing neck muscles. Although previous image registration efforts form a strong basis for many medical applications, none can satisfy the requirements of all of them because of the challenges associated with their implementation and low accuracy which could be due to anatomical complexities and variabilities or the artefacts of imaging devices. In existing methods, multi-resolution- and heuristic-based methods are popular. However, the above issues cause conventional multi-resolution-based registration methods to be trapped in local minima due to their low degrees of freedom in their geometrical transforms. Although heuristic-based methods are good at handling large mismatches, they require pre-segmentation and are computationally expensive. Also, current deformable methods often face statistical instability problems and many local optima when dealing with small mismatches. On the other hand, deep learning-based methods have achieved significant success over the last few years. Although a deeper network can learn more complex features and yields better performances, its depth cannot be increased as this would cause the gradient to vanish during training and result in training difficulties. Recently, researchers have focused on attention mechanisms for deep learning but current attention models face a challenge in the case of an application with compact and similar small multiple classes, large variability, low contrast and noise. The focus of this dissertation is on the design of 3D-3D image registration approaches as well as deep learning-based semantic segmentation methods for analyzing neck muscles. In the first part of this thesis, a novel object-constrained hierarchical registration framework for aligning inter-subject neck muscles is proposed. Firstly, to handle large-scale local minima, it uses a coarse registration technique which optimizes a new edge position difference (EPD) similarity measure to align large mismatches. Also, a new transformation based on the discrete periodic spline wavelet (DPSW), affine and free-form-deformation (FFD) transformations are exploited. Secondly, to avoid the monotonous nature of using transformations in multiple stages, affine registration technique, which uses a double-pushing system by changing the edges in the EPD and switching the transformation's resolutions, is designed to align small mismatches. The EPD helps in both the coarse and fine techniques to implement object-constrained registration via controlling edges which is not possible using traditional similarity measures. Experiments are performed on clinical 3D magnetic resonance imaging (MRI) scans of the neck, with the results showing that the EPD is more effective than the mutual information (MI) and the sum of squared difference (SSD) measures in terms of the volumetric dice similarity coefficient (DSC). Also, the proposed method is compared with two state-of-the-art approaches with ablation studies of inter-subject deformable registration and achieves better accuracy, robustness and consistency. However, as this method is computationally complex and has a problem handling large-scale anatomical variabilities, another 3D-3D registration framework with two novel contributions is proposed in the second part of this thesis. Firstly, a two-stage heuristic search optimization technique for handling large mismatches,which uses a minimal user hypothesis regarding these mismatches and is computationally fast, is introduced. It brings a moving image hierarchically closer to a fixed one using MI and EPD similarity measures in the coarse and fine stages, respectively, while the images do not require pre-segmentation as is necessary in traditional heuristic optimization-based techniques. Secondly, a region of interest (ROI) EPD-based registration framework for handling small mismatches using salient anatomical information (AI), in which a convex objective function is formed through a unique shape created from the desired objects in the ROI, is proposed. It is compared with two state-of-the-art methods on a neck dataset, with the results showing that it is superior in terms of accuracy and is computationally fast. In the last part of this thesis, an evaluation study of recent U-Net-based convolutional neural networks (CNNs) is performed on a neck dataset. It comprises 6 recent models, the U-Net, U-Net with a conditional random field (CRF-Unet), attention U-Net (A-Unet), nested U-Net or U-Net++, multi-feature pyramid (MFP)-Unet and recurrent residual U-Net (R2Unet) and 4 with more comprehensive modifications, the multi-scale U-Net (MS-Unet), parallel multi-scale U-Net (PMSUnet), recurrent residual attention U-Net (R2A-Unet) and R2A-Unet++ in neck muscles segmentation, with analyses of the numerical results indicating that the R2Unet architecture achieves the best accuracy. Also, two deep learning-based semantic segmentation approaches are proposed. In the first, a new two-stage U-Net++ (TS-UNet++) uses two different types of deep CNNs (DCNNs) rather than one similar to the traditional multi-stage method, with the U-Net++ in the first stage and the U-Net in the second. More convolutional blocks are added after the input and before the output layers of the multi-stage approach to better extract the low- and high-level features. A new concatenation-based fusion structure, which is incorporated in the architecture to allow deep supervision, helps to increase the depth of the network without accelerating the gradient-vanishing problem. Then, more convolutional layers are added after each concatenation of the fusion structure to extract more representative features. The proposed network is compared with the U-Net, U-Net++ and two-stage U-Net (TS-UNet) on the neck dataset, with the results indicating that it outperforms the others. In the second approach, an explicit attention method, in which the attention is performed through a ROI evolved from ground truth via dilation, is proposed. It does not require any additional CNN, as does a cascaded approach, to localize the ROI. Attention in a CNN is sensitive with respect to the area of the ROI. This dilated ROI is more capable of capturing relevant regions and suppressing irrelevant ones than a bounding box and region-level coarse annotation, and is used during training of any CNN. Coarse annotation, which does not require any detailed pixel wise delineation that can be performed by any novice person, is used during testing. This proposed ROI-based attention method, which can handle compact and similar small multiple classes with objects with large variabilities, is compared with the automatic A-Unet and U-Net, and performs best

Non-rigid medical image registration with extended free form deformations: modelling general tissue transitions

Image registration seeks pointwise correspondences between the same or analogous objects in different images. Conventional registration methods generally impose continuity and smoothness throughout the image. However, there are cases in which the deformations may involve discontinuities. In general, the discontinuities can be of different types, depending on the physical properties of the tissue transitions involved and boundary conditions. For instance, in the respiratory motion the lungs slide along the thoracic cage following the tangential direction of their interface. In the normal direction, however, the lungs and the thoracic cage are constrained to be always in contact but they have different material properties producing different compression or expansion rates. In the literature, there is no generic method, which handles different types of discontinuities and considers their directional dependence. The aim of this thesis is to develop a general registration framework that is able to correctly model different types of tissue transitions with a general formalism. This has led to the development of the eXtended Free Form Deformation (XFFD) registration method. XFFD borrows the concept of the interpolation method from the eXtended Finite Element method (XFEM) to incorporate discontinuities by enriching B-spline basis functions, coupled with extra degrees of freedom. XFFD can handle different types of discontinuities and encodes their directional-dependence without any additional constraints. XFFD has been evaluated on digital phantoms, publicly available 3D liver and lung CT images. The experiments show that XFFD improves on previous methods and that it is important to employ the correct model that corresponds to the discontinuity type involved at the tissue transition. The effect of using incorrect models is more evident in the strain, which measures mechanical properties of the tissues

Bayesian generative learning of brain and spinal cord templates from neuroimaging datasets

In the field of neuroimaging, Bayesian modelling techniques have been largely adopted and recognised as powerful tools for the purpose of extracting quantitative anatomical and functional information from medical scans. Nevertheless the potential of Bayesian inference has not yet been fully exploited, as many available tools rely on point estimation techniques, such as maximum likelihood estimation, rather than on full Bayesian inference. The aim of this thesis is to explore the value of approximate learning schemes, for instance variational Bayes, to perform inference from brain and spinal cord MRI data. The applications that will be explored in this work mainly concern image segmentation and atlas construction, with a particular emphasis on the problem of shape and intensity prior learning, from large training data sets of structural MR scans. The resulting computational tools are intended to enable integrated brain and spinal cord morphometric analyses, as opposed to the approach that is most commonly adopted in neuroimaging, which consists in optimising separate tools for brain and spine morphometrics

Automatic Segmentation of Intramedullary Multiple Sclerosis Lesions

Contexte: La moelle épinière est un composant essentiel du système nerveux central. Elle contient des neurones responsables d’importantes fonctionnalités et assure la transmission d’informations motrices et sensorielles entre le cerveau et le système nerveux périphérique. Un endommagement de la moelle épinière, causé par un choc ou une maladie neurodégénérative, peut mener à un sérieux handicap, pouvant entraîner des incapacités fonctionnelles, de la paralysie et/ou de la douleur. Chez les patients atteints de sclérose en plaques (SEP), la moelle épinière est fréquemment affectée par de l’atrophie et/ou des lésions. L’imagerie par résonance magnétique (IRM) conventionnelle est largement utilisée par des chercheurs et des cliniciens pour évaluer et caractériser, de façon non-invasive, des altérations micro-structurelles. Une évaluation quantitative des atteintes structurelles portées à la moelle épinière (e.g. sévérité de l’atrophie, extension des lésions) est essentielle pour le diagnostic, le pronostic et la supervision sur le long terme de maladies, telles que la SEP. De plus, le développement de biomarqueurs impartiaux est indispensable pour évaluer l’effet de nouveaux traitements thérapeutiques. La segmentation de la moelle épinière et des lésions intramédullaires de SEP sont, par conséquent, pertinentes d’un point de vue clinique, aussi bien qu’une étape nécessaire vers l’interprétation d’images RM multiparamétriques. Cependant, la segmentation manuelle est une tâche extrêmement chronophage, fastidieuse et sujette à des variations inter- et intra-expert. Il y a par conséquent un besoin d’automatiser les méthodes de segmentations, ce qui pourrait faciliter l’efficacité procédures d’analyses. La segmentation automatique de lésions est compliqué pour plusieurs raisons: (i) la variabilité des lésions en termes de forme, taille et position, (ii) les contours des lésions sont la plupart du temps difficilement discernables, (iii) l’intensité des lésions sur des images MR sont similaires à celles de structures visiblement saines. En plus de cela, réaliser une segmentation rigoureuse sur l’ensemble d’une base de données multi-centrique d’IRM est rendue difficile par l’importante variabilité des protocoles d’acquisition (e.g. résolution, orientation, champ de vue de l’image). Malgré de considérables récents développements dans le traitement d’images MR de moelle épinière, il n’y a toujours pas de méthode disponible pouvant fournir une segmentation rigoureuse et fiable de la moelle épinière pour un large spectre de pathologies et de protocoles d’acquisition. Concernant les lésions intramédullaires, une recherche approfondie dans la littérature n’a pas pu fournir une méthode disponible de segmentation automatique. Objectif: Développer un système complètement automatique pour segmenter la moelle épinière et les lésions intramédullaires sur des IRM conventionnelles humaines. Méthode: L’approche présentée est basée de deux réseaux de neurones à convolution mis en cascade. La méthode a été pensée pour faire face aux principaux obstacles que présentent les données IRM de moelle épinière. Le procédé de segmentation a été entrainé et validé sur une base de données privée composée de 1943 images, acquises dans 30 différents centres avec des protocoles hétérogènes. Les sujets scannés comportent 459 sujets sains, 471 patients SEP et 112 avec d’autres pathologies affectant la moelle épinière. Le module de segmentation de la moelle épinière a été comparé à une méthode existante reconnue par la communauté, PropSeg. Résultats: L’approche basée sur les réseaux de neurones à convolution a fourni de meilleurs résultats que PropSeg, atteignant un Dice médian (intervalle inter-quartiles) de 94.6 (4.6) vs. 87.9 (18.3) %. Pour les lésions, notre segmentation automatique a permis d'obtenir un Dice de 60.0 (21.4) % en le comparant à la segmentation manuelle, un ratio de vrai positifs de 83 (34) %, et une précision de 77 (44) %. Conclusion: Une méthode complètement automatique et innovante pour segmenter la moelle épinière et les lésions SEP intramédullaires sur des données IRM a été conçue durant ce projet de maîtrise. La méthode a été abondamment validée sur une base de données clinique. La robustesse de la méthode de segmentation de moelle épinière a été démontrée, même sur des cas pathologiques. Concernant la segmentation des lésions, les résultats sont encourageants, malgré un taux de faux positifs relativement élevé. Je crois en l’impact que peut potentiellement avoir ces outils pour la communauté de chercheurs. Dans cette optique, les méthodes ont été intégrées et documentées dans un logiciel en accès-ouvert, la “Spinal Cord Toolbox”. Certains des outils développés pendant ce projet de Maîtrise sont déjà utilisés par des analyses d’études cliniques, portant sur des patients SEP et sclérose latérale amyotrophique.----------ABSTRACT Context: The spinal cord is a key component of the central nervous system, which contains neurons responsible for complex functions, and ensures the conduction of motor and sensory information between the brain and the peripheral nervous system. Damage to the spinal cord, through trauma or neurodegenerative diseases, can lead to severe impairment, including functional disabilities, paralysis and/or pain. In multiple sclerosis (MS) patients, the spinal cord is frequently affected by atrophy and/or lesions. Conventional magnetic resonance imaging (MRI) is widely used by researchers and clinicians to non-invasively assess and characterize spinal cord microstructural changes. Quantitative assessment of the structural damage to the spinal cord (e.g. atrophy severity, lesion extent) is essential for the diagnosis, prognosis and longitudinal monitoring of diseases, such as MS. Furthermore, the development of objective biomarkers is essential to evaluate the effect of new therapeutic treatments. Spinal cord and intramedullary MS lesions segmentation is consequently clinically relevant, as well as a necessary step towards the interpretation of multi-parametric MR images. However, manual segmentation is highly time-consuming, tedious and prone to intra- and inter-rater variability. There is therefore a need for automated segmentation methods to facilitate the efficiency of analysis pipelines. Automatic lesion segmentation is challenging for various reasons: (i) lesion variability in terms of shape, size and location, (ii) lesion boundaries are most of the time not well defined, (iii) lesion intensities on MR data are confounding with those of normal-appearing structures. Moreover, achieving robust segmentation across multi-center MRI data is challenging because of the broad variability of data features (e.g. resolution, orientation, field of view). Despite recent substantial developments in spinal cord MRI processing, there is still no method available that can yield robust and reliable spinal cord segmentation across the very diverse spinal pathologies and data features. Regarding the intramedullary lesions, a thorough search of the relevant literature did not yield available method of automatic segmentation. Goal: To develop a fully-automatic framework for segmenting the spinal cord and intramedullary MS lesions from conventional human MRI data. Method: The presented approach is based on a cascade of two Convolutional Neural Networks (CNN). The method has been designed to face the main challenges of ‘real world’ spinal cord MRI data. It was trained and validated on a private dataset made up of 1943 MR volumes, acquired in different 30 sites with heterogeneous acquisition protocols. Scanned subjects involve 459 healthy controls, 471 MS patients and 112 with other spinal pathologies. The proposed spinal cord segmentation method was compared to a state-of-the-art spinal cord segmentation method, PropSeg. Results: The CNN-based approach achieved better results than PropSeg, yielding a median (interquartile range) Dice of 94.6 (4.6) vs. 87.9 (18.3) % when compared to the manual segmentation. For the lesion segmentation task, our method provided a median Dice-overlap with the manual segmentation of 60.0 (21.4) %, a lesion-based true positive rate of 83 (34) % and a lesion-based precision de 77 (44) %. Conclusion: An original fully-automatic method to segment the spinal cord and intramedullary MS lesions on MRI data has been devised during this Master’s project. The method was validated extensively against a clinical dataset. The robustness of the spinal cord segmentation has been demonstrated, even on challenging pathological cases. Regarding the lesion segmentation, the results are encouraging despite the fairly high false positive rate. I believe in the potential value of these developed tools for the research community. In this vein, the methods are integrated and documented into an open-source software, the Spinal Cord Toolbox. Some of the tools developed during this Master’s project are already integrated into automated analysis pipelines of clinical studies, including MS and Amyotrophic Lateral Sclerosis patients

ADVANCED MOTION MODELS FOR RIGID AND DEFORMABLE REGISTRATION IN IMAGE-GUIDED INTERVENTIONS

Image-guided surgery (IGS) has been a major area of interest in recent decades that continues to transform surgical interventions and enable safer, less invasive procedures. In the preoperative contexts, diagnostic imaging, including computed tomography (CT) and magnetic resonance (MR) imaging, offers a basis for surgical planning (e.g., definition of target, adjacent anatomy, and the surgical path or trajectory to the target). At the intraoperative stage, such preoperative images and the associated planning information are registered to intraoperative coordinates via a navigation system to enable visualization of (tracked) instrumentation relative to preoperative images. A major limitation to such an approach is that motions during surgery, either rigid motions of bones manipulated during orthopaedic surgery or brain soft-tissue deformation in neurosurgery, are not captured, diminishing the accuracy of navigation systems. This dissertation seeks to use intraoperative images (e.g., x-ray fluoroscopy and cone-beam CT) to provide more up-to-date anatomical context that properly reflects the state of the patient during interventions to improve the performance of IGS. Advanced motion models for inter-modality image registration are developed to improve the accuracy of both preoperative planning and intraoperative guidance for applications in orthopaedic pelvic trauma surgery and minimally invasive intracranial neurosurgery. Image registration algorithms are developed with increasing complexity of motion that can be accommodated (single-body rigid, multi-body rigid, and deformable) and increasing complexity of registration models (statistical models, physics-based models, and deep learning-based models). For orthopaedic pelvic trauma surgery, the dissertation includes work encompassing: (i) a series of statistical models to model shape and pose variations of one or more pelvic bones and an atlas of trajectory annotations; (ii) frameworks for automatic segmentation via registration of the statistical models to preoperative CT and planning of fixation trajectories and dislocation / fracture reduction; and (iii) 3D-2D guidance using intraoperative fluoroscopy. For intracranial neurosurgery, the dissertation includes three inter-modality deformable registrations using physic-based Demons and deep learning models for CT-guided and CBCT-guided procedures