21 research outputs found

    Similarity search applications in medical images

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    Landmark Localization, Feature Matching and Biomarker Discovery from Magnetic Resonance Images

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    The work presented in this thesis proposes several methods that can be roughly divided into three different categories: I) landmark localization in medical images, II) feature matching for image registration, and III) biomarker discovery in neuroimaging. The first part deals with the identification of anatomical landmarks. The motivation stems from the fact that the manual identification and labeling of these landmarks is very time consuming and prone to observer errors, especially when large datasets must be analyzed. In this thesis we present three methods to tackle this challenge: A landmark descriptor based on local self-similarities (SS), a subspace building framework based on manifold learning and a sparse coding landmark descriptor based on data-specific learned dictionary basis. The second part of this thesis deals with finding matching features between a pair of images. These matches can be used to perform a registration between them. Registration is a powerful tool that allows mapping images in a common space in order to aid in their analysis. Accurate registration can be challenging to achieve using intensity based registration algorithms. Here, a framework is proposed for learning correspondences in pairs of images by matching SS features and random sample and consensus (RANSAC) is employed as a robust model estimator to learn a deformation model based on feature matches. Finally, the third part of the thesis deals with biomarker discovery using machine learning. In this section a framework for feature extraction from learned low-dimensional subspaces that represent inter-subject variability is proposed. The manifold subspace is built using data-driven regions of interest (ROI). These regions are learned via sparse regression, with stability selection. Also, probabilistic distribution models for different stages in the disease trajectory are estimated for different class populations in the low-dimensional manifold and used to construct a probabilistic scoring function.Open Acces

    Automatic Segmentation of the Lumbar Spine from Medical Images

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    Segmentation of the lumbar spine in 3D is a necessary step in numerous medical applications, but remains a challenging problem for computational methods due to the complex and varied shape of the anatomy and the noise and other artefacts often present in the images. While manual annotation of anatomical objects such as vertebrae is often carried out with the aid of specialised software, obtaining even a single example can be extremely time-consuming. Automating the segmentation process is the only feasible way to obtain accurate and reliable segmentations on any large scale. This thesis describes an approach for automatic segmentation of the lumbar spine from medical images; specifically those acquired using magnetic resonance imaging (MRI) and computed tomography (CT). The segmentation problem is formulated as one of assigning class labels to local clustered regions of an image (called superpixels in 2D or supervoxels in 3D). Features are introduced in 2D and 3D which can be used to train a classifier for estimating the class labels of the superpixels or supervoxels. Spatial context is introduced by incorporating the class estimates into a conditional random field along with a learned pairwise metric. Inference over the resulting model can be carried out very efficiently, enabling an accurate pixel- or voxel-level segmentation to be recovered from the labelled regions. In contrast to most previous work in the literature, the approach does not rely on explicit prior shape information. It therefore avoids many of the problems associated with these methods, such as the need to construct a representative prior model of anatomical shape from training data and the approximate nature of the optimisation. The general-purpose nature of the proposed method means that it can be used to accurately segment both vertebrae and intervertebral discs from medical images without fundamental change to the model. Evaluation of the approach shows it to obtain accurate and robust performance in the presence of significant anatomical variation. The median average symmetric surface distances for 2D vertebra segmentation were 0.27mm on MRI data and 0.02mm on CT data. For 3D vertebra segmentation the median surface distances were 0.90mm on MRI data and 0.20mm on CT data. For 3D intervertebral disc segmentation a median surface distance of 0.54mm was obtained on MRI data

    LIPIcs, Volume 244, ESA 2022, Complete Volume

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    LIPIcs, Volume 244, ESA 2022, Complete Volum

    Recalage déformable à base de graphes : mise en correspondance coupe-vers-volume et méthodes contextuelles

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    Image registration methods, which aim at aligning two or more images into one coordinate system, are among the oldest and most widely used algorithms in computer vision. Registration methods serve to establish correspondence relationships among images (captured at different times, from different sensors or from different viewpoints) which are not obvious for the human eye. A particular type of registration algorithm, known as graph-based deformable registration methods, has become popular during the last decade given its robustness, scalability, efficiency and theoretical simplicity. The range of problems to which it can be adapted is particularly broad. In this thesis, we propose several extensions to the graph-based deformable registration theory, by exploring new application scenarios and developing novel methodological contributions.Our first contribution is an extension of the graph-based deformable registration framework, dealing with the challenging slice-to-volume registration problem. Slice-to-volume registration aims at registering a 2D image within a 3D volume, i.e. we seek a mapping function which optimally maps a tomographic slice to the 3D coordinate space of a given volume. We introduce a scalable, modular and flexible formulation accommodating low-rank and high order terms, which simultaneously selects the plane and estimates the in-plane deformation through a single shot optimization approach. The proposed framework is instantiated into different variants based on different graph topology, label space definition and energy construction. Simulated and real-data in the context of ultrasound and magnetic resonance registration (where both framework instantiations as well as different optimization strategies are considered) demonstrate the potentials of our method.The other two contributions included in this thesis are related to how semantic information can be encompassed within the registration process (independently of the dimensionality of the images). Currently, most of the methods rely on a single metric function explaining the similarity between the source and target images. We argue that incorporating semantic information to guide the registration process will further improve the accuracy of the results, particularly in the presence of semantic labels making the registration a domain specific problem.We consider a first scenario where we are given a classifier inferring probability maps for different anatomical structures in the input images. Our method seeks to simultaneously register and segment a set of input images, incorporating this information within the energy formulation. The main idea is to use these estimated maps of semantic labels (provided by an arbitrary classifier) as a surrogate for unlabeled data, and combine them with population deformable registration to improve both alignment and segmentation.Our last contribution also aims at incorporating semantic information to the registration process, but in a different scenario. In this case, instead of supposing that we have pre-trained arbitrary classifiers at our disposal, we are given a set of accurate ground truth annotations for a variety of anatomical structures. We present a methodological contribution that aims at learning context specific matching criteria as an aggregation of standard similarity measures from the aforementioned annotated data, using an adapted version of the latent structured support vector machine (LSSVM) framework.Les mĂ©thodes de recalage d’images, qui ont pour but l’alignement de deux ou plusieurs images dans un mĂȘme systĂšme de coordonnĂ©es, sont parmi les algorithmes les plus anciens et les plus utilisĂ©s en vision par ordinateur. Les mĂ©thodes de recalage servent Ă  Ă©tablir des correspondances entre des images (prises Ă  des moments diffĂ©rents, par diffĂ©rents senseurs ou avec diffĂ©rentes perspectives), lesquelles ne sont pas Ă©videntes pour l’Ɠil humain. Un type particulier d’algorithme de recalage, connu comme « les mĂ©thodes de recalage dĂ©formables Ă  l’aide de modĂšles graphiques » est devenu de plus en plus populaire ces derniĂšres annĂ©es, grĂące Ă  sa robustesse, sa scalabilitĂ©, son efficacitĂ© et sa simplicitĂ© thĂ©orique. La gamme des problĂšmes auxquels ce type d’algorithme peut ĂȘtre adaptĂ© est particuliĂšrement vaste. Dans ce travail de thĂšse, nous proposons plusieurs extensions Ă  la thĂ©orie de recalage dĂ©formable Ă  l’aide de modĂšles graphiques, en explorant de nouvelles applications et en dĂ©veloppant des contributions mĂ©thodologiques originales.Notre premiĂšre contribution est une extension du cadre du recalage Ă  l’aide de graphes, en abordant le problĂšme trĂšs complexe du recalage d’une tranche avec un volume. Le recalage d’une tranche avec un volume est le recalage 2D dans un volume 3D, comme par exemple le mapping d’une tranche tomographique dans un systĂšme de coordonnĂ©es 3D d’un volume en particulier. Nos avons proposĂ© une formulation scalable, modulaire et flexible pour accommoder des termes d'ordre Ă©levĂ© et de rang bas, qui peut sĂ©lectionner le plan et estimer la dĂ©formation dans le plan de maniĂšre simultanĂ©e par une seule approche d'optimisation. Le cadre proposĂ© est instanciĂ© en diffĂ©rentes variantes, basĂ©s sur diffĂ©rentes topologies du graph, dĂ©finitions de l'espace des Ă©tiquettes et constructions de l'Ă©nergie. Le potentiel de notre mĂ©thode a Ă©tĂ© dĂ©montrĂ© sur des donnĂ©es rĂ©elles ainsi que des donnĂ©es simulĂ©es dans le cadre d’une rĂ©sonance magnĂ©tique d’ultrason (oĂč le cadre d’installation et les stratĂ©gies d’optimisation ont Ă©tĂ© considĂ©rĂ©s).Les deux autres contributions inclues dans ce travail de thĂšse, sont liĂ©es au problĂšme de l’intĂ©gration de l’information sĂ©mantique dans la procĂ©dure de recalage (indĂ©pendamment de la dimensionnalitĂ© des images). Actuellement, la plupart des mĂ©thodes comprennent une seule fonction mĂ©trique pour expliquer la similaritĂ© entre l’image source et l’image cible. Nous soutenons que l'intĂ©gration des informations sĂ©mantiques pour guider la procĂ©dure de recalage pourra encore amĂ©liorer la prĂ©cision des rĂ©sultats, en particulier en prĂ©sence d'Ă©tiquettes sĂ©mantiques faisant du recalage un problĂšme spĂ©cifique adaptĂ© Ă  chaque domaine.Nous considĂ©rons un premier scĂ©nario en proposant un classificateur pour infĂ©rer des cartes de probabilitĂ© pour les diffĂ©rentes structures anatomiques dans les images d'entrĂ©e. Notre mĂ©thode vise Ă  recaler et segmenter un ensemble d'images d'entrĂ©e simultanĂ©ment, en intĂ©grant cette information dans la formulation de l'Ă©nergie. L'idĂ©e principale est d'utiliser ces cartes estimĂ©es des Ă©tiquettes sĂ©mantiques (fournie par un classificateur arbitraire) comme un substitut pour les donnĂ©es non-Ă©tiquettĂ©es, et les combiner avec le recalage dĂ©formable pour amĂ©liorer l'alignement ainsi que la segmentation.Notre derniĂšre contribution vise Ă©galement Ă  intĂ©grer l'information sĂ©mantique pour la procĂ©dure de recalage, mais dans un scĂ©nario diffĂ©rent. Dans ce cas, au lieu de supposer que nous avons des classificateurs arbitraires prĂ©-entraĂźnĂ©s Ă  notre disposition, nous considĂ©rons un ensemble d’annotations prĂ©cis (vĂ©ritĂ© terrain) pour une variĂ©tĂ© de structures anatomiques. Nous prĂ©sentons une contribution mĂ©thodologique qui vise Ă  l'apprentissage des critĂšres correspondants au contexte spĂ©cifique comme une agrĂ©gation des mesures de similaritĂ© standard Ă  partir des donnĂ©es annotĂ©es, en utilisant une adaptation de l’algorithme « Latent Structured Support Vector Machine »

    Méthodes multi-organes rapides avec a priori de forme pour la localisation et la segmentation en imagerie médicale 3D

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    With the ubiquity of imaging in medical applications (diagnostic, treatment follow-up, surgery planning. . . ), image processing algorithms have become of primary importance. Algorithms help clinicians extract critical information more quickly and more reliably from increasingly large and complex acquisitions. In this context, anatomy localization and segmentation is a crucial component in modern clinical workflows. Due to particularly high requirements in terms of robustness, accuracy and speed, designing such tools remains a challengingtask.In this work, we propose a complete pipeline for the segmentation of multiple organs in medical images. The method is generic, it can be applied to varying numbers of organs, on different imaging modalities. Our approach consists of three components: (i) an automatic localization algorithm, (ii) an automatic segmentation algorithm, (iii) a framework for interactive corrections. We present these components as a coherent processing chain, although each block could easily be used independently of the others. To fulfill clinical requirements, we focus on robust and efficient solutions. Our anatomy localization method is based on a cascade of Random Regression Forests (Cuingnet et al., 2012). One key originality of our work is the use of shape priors for each organ (thanks to probabilistic atlases). Combined with the evaluation of the trained regression forests, they result in shape-consistent confidence maps for each organ instead of simple bounding boxes. Our segmentation method extends the implicit template deformation framework of Mory et al. (2012) to multiple organs. The proposed formulation builds on the versatility of the original approach and introduces new non-overlapping constraintsand contrast-invariant forces. This makes our approach a fully automatic, robust and efficient method for the coherent segmentation of multiple structures. In the case of imperfect segmentation results, it is crucial to enable clinicians to correct them easily. We show that our automatic segmentation framework can be extended with simple user-driven constraints to allow for intuitive interactive corrections. We believe that this final component is key towards the applicability of our pipeline in actual clinical routine.Each of our algorithmic components has been evaluated on large clinical databases. We illustrate their use on CT, MRI and US data and present a user study gathering the feedback of medical imaging experts. The results demonstrate the interest in our method and its potential for clinical use.Avec l’utilisation de plus en plus rĂ©pandue de l’imagerie dans la pratique mĂ©dicale (diagnostic, suivi, planification d’intervention, etc.), le dĂ©veloppement d’algorithmes d’analyse d’images est devenu primordial. Ces algorithmes permettent aux cliniciens d’analyser et d’interprĂ©ter plus facilement et plus rapidement des donnĂ©es de plus en plus complexes. Dans ce contexte, la localisation et la segmentation de structures anatomiques sont devenues des composants critiques dans les processus cliniques modernes. La conception de tels outils pour rĂ©pondre aux exigences de robustesse, prĂ©cision et rapiditĂ© demeure cependant un rĂ©el dĂ©fi technique.Ce travail propose une mĂ©thode complĂšte pour la segmentation de plusieurs organes dans des images mĂ©dicales. Cette mĂ©thode, gĂ©nĂ©rique et pouvant ĂȘtre appliquĂ©e Ă  un nombre variĂ© de structures et dans diffĂ©rentes modalitĂ©s d’imagerie, est constituĂ©e de trois composants : (i) un algorithme de localisation automatique, (ii) un algorithme de segmentation, (iii) un outil de correction interactive. Ces diffĂ©rentes parties peuvent s’enchaĂźner aisĂ©ment pour former un outil complet et cohĂ©rent, mais peuvent aussi bien ĂȘtre utilisĂ©es indĂ©pendemment. L’accent a Ă©tĂ© mis sur des mĂ©thodes robustes et efficaces afin de rĂ©pondre aux exigences cliniques. Notre mĂ©thode de localisation s’appuie sur une cascade de rĂ©gression par forĂȘts alĂ©atoires (Cuingnet et al., 2012). Elle introduit l’utilisation d’informations a priori de forme, spĂ©cifiques Ă  chaque organe (grĂące Ă  des atlas probabilistes) pour des rĂ©sultats plus cohĂ©rents avec la rĂ©alitĂ© anatomique. Notre mĂ©thode de segmentation Ă©tend la mĂ©thode de segmentation par modĂšle implicite (Mory et al., 2012) Ă  plusieurs modĂšles. La formulation proposĂ©e permet d’obtenir des dĂ©formations cohĂ©rentes, notamment en introduisant des contraintes de non recouvrement entre les modĂšles dĂ©formĂ©s. En s’appuyant sur des forces images polyvalentes, l’approche proposĂ©e se montre robuste et performante pour la segmentation de multiples structures. Toute mĂ©thode automatique n’est cependant jamais parfaite. Afin que le clinicien garde la main sur le rĂ©sultat final, nous proposons d’enrichir la formulation prĂ©cĂ©dente avec des contraintes fournies par l’utilisateur. Une optimisation localisĂ©e permet d’obtenir un outil facile Ă  utiliser et au comportement intuitif. Ce dernier composant est crucial pour que notre outil soit rĂ©ellement utilisable en pratique. Chacun de ces trois composants a Ă©tĂ© Ă©valuĂ© sur plusieurs grandes bases de donnĂ©es cliniques (en tomodensitomĂ©trie, imagerie par rĂ©sonance magnĂ©tique et ultrasons). Une Ă©tude avec des utilisateurs nous a aussi permis de recueillir des retours positifs de plusieurs experts en imagerie mĂ©dicale. Les diffĂ©rents rĂ©sultats prĂ©sentĂ©s dans ce manuscrit montrent l’intĂ©rĂȘt de notre mĂ©thode et son potentiel pour une utilisation clinique

    Proceedings of the Fourth International Workshop on Mathematical Foundations of Computational Anatomy - Geometrical and Statistical Methods for Biological Shape Variability Modeling (MFCA 2013), Nagoya, Japan

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    International audienceComputational anatomy is an emerging discipline at the interface of geometry, statistics and image analysis which aims at modeling and analyzing the biological shape of tissues and organs. The goal is to estimate representative organ anatomies across diseases, populations, species or ages, to model the organ development across time (growth or aging), to establish their variability, and to correlate this variability information with other functional, genetic or structural information. The Mathematical Foundations of Computational Anatomy (MFCA) workshop aims at fostering the interactions between the mathematical community around shapes and the MICCAI community in view of computational anatomy applications. It targets more particularly researchers investigating the combination of statistical and geometrical aspects in the modeling of the variability of biological shapes. The workshop is a forum for the exchange of the theoretical ideas and aims at being a source of inspiration for new methodological developments in computational anatomy. A special emphasis is put on theoretical developments, applications and results being welcomed as illustrations. Following the first edition of this workshop in 2006, second edition in New-York in 2008, the third edition in Toronto in 2011, the forth edition was held in Nagoya Japan on September 22 2013

    Detection of anatomical structures in medical datasets

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    Detection and localisation of anatomical structures is extremely helpful for many image analysis algorithms. This thesis is concerned with the automatic identiïŹcation of landmark points, anatomical regions and vessel centre lines in three-dimensional medical datasets. We examine how machine learning and atlas-based ideas may be combined to produce efïŹcient, context-aware algorithms. For the problem of anatomical landmark detection, we develop an analog to the idea of autocontext, termed atlas location autocontext, whereby spatial context is iteratively learnt by the machine learning algorithm as part of a feedback loop. We then extend our anatomical landmark detection algorithm from Computed Tomography to Magnetic Resonance images, using image features based on histograms of oriented gradients. A cross-modality landmark detector is demonstrated using unsigned gradient orientations. The problem of brain parcellation is approached by independently training a random forest and a multi-atlas segmentation algorithm, then combining them by a simple Bayesian product operation. It is shown that, given classiïŹers providing complementary information, the hybrid classiïŹer provides a superior result. The Bayesian product method of combination outperforms simple averaging where the classiïŹers are sufïŹciently independent. Finally, we present a system for identifying and tracking major arteries in Magnetic Resonance Angiography datasets, using automatically detected vascular landmarks to seed the tracking. Knowledge of individual vessel characteristics is employed to guide the tracking algorithm by two means. Firstly, the data is pre-processed using a top-hat transform of size corresponding to the vessel diameter. Secondly, a vascular atlas is generated to inform the cost function employed in the minimum path algorithm. Fully automatic tracking of the major arteries of the body is satisfactorily demonstrated
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