Shape analysis for 3D segmentation of cerebral structures with level sets and fuzzy control

http://www.afrif.asso.fr/archive/rfia2006/index.htmlNous nous proposons de segmenter des structures 3D avec des ensembles de niveau dont l'évolution est guidée par un modèle de forme et gérée par commande oue. Pour cela, plusieurs contours évoluent simultanément en direction de cibles anatomiques dénies au préalable. Un système de décision oue combine l'information a priori fournie par un modèle de forme, utilisé comme atlas, avec la distribution d'intensité de l'image et les positions relatives des contours. Il donne en sortie le terme de direction de l'équation d'évolution de l'ensemble de niveau associé à chaque contour. Cela entraîne une expansion ou une contraction locale des contours, qui convergent nalement vers leurs cibles respectives. Le modèle de forme est construit par analyse en composantes principales. La forme moyenne et les variations obtenues permettent alors de localiser une cible et de déterminer les états ous caractérisant la distance du contour courant à cette cible. Cette méthode est appliquée à la segmentation des noyaux gris du cerveau et évaluée quantitativement sur une base de 18 sujets

Symmetrical EEG-FMRI Imaging by Sparse Regularization

International audienceThis work considers the problem of brain imaging using simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). To this end, we introduce a linear coupling model that links the electrical EEG signal to the hemodynamic response from the blood-oxygen level dependent (BOLD) signal. Both modalities are then symmetrically integrated, to achieve a high resolution in time and space while allowing some robustness against potential decoupling of the BOLD effect. The novelty of the approach consists in expressing the joint imaging problem as a linear inverse problem, which is addressed using sparse regularization. We consider several sparsity-enforcing penalties, which naturally reflect the fact that only few areas of the brain are activated at a certain time, and allow for a fast optimization through proximal algorithms. The significance of the method and the effectiveness of the algorithms are demonstrated through numerical investigations on a spherical head model

3D Rigid Registration of Intraoperative Ultrasound and Preoperative MR Brain Images Based on Hyperechogenic Structures

The registration of intraoperative ultrasound (US) images with preoperative magnetic resonance (MR) images is a challenging problem due to the difference of information contained in each image modality. To overcome this difficulty, we introduce a new probabilistic function based on the matching of cerebral hyperechogenic structures. In brain imaging, these structures are the liquid interfaces such as the cerebral falx and the sulci, and the lesions when the corresponding tissue is hyperechogenic. The registration procedure is achieved by maximizing the joint probability for a voxel to be included in hyperechogenic structures in both modalities. Experiments were carried out on real datasets acquired during neurosurgical procedures. The proposed validation framework is based on (i) visual assessment, (ii) manual expert estimations , and (iii) a robustness study. Results show that the proposed method (i) is visually efficient, (ii) produces no statistically different registration accuracy compared to manual-based expert registration, and (iii) converges robustly. Finally, the computation time required by our method is compatible with intraoperative use

Quality Assessment of Electromagnetic Localizers in the Context of 3D Ultrasound

Electromagnetic spatial localizers represent low cost and flexible solution for clinical free-hand three dimensional ultrasound system. Their performances condition the accuracy of the calibration and the ultrasound reconstructed volume and the robustness of the acquisition stage. In this study, we evaluate the resolution and the accuracy for translations and rotations of two widespread localizers in quite metallic environment: Fastrak and Flock of Bird. Our experiments show that their resolution for position is under 0.2mm at 60cm, and their resolution for orientation is around 0.06 degrees. For translations below 30cm, their position accuracy is around 1.5mm, and the angular accuracy is under 1 degree. These results show that theses devices provide sufficient accuracy for use in a clinical context

3D Wavelet Subbands Mixing for Image Denoising

A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. The method proposed in this paper is a fully automatic 3D blockwise version of the nonlocal (NL) means filter with wavelet subbands mixing. The proposed wavelet subbands mixing is based on a multiresolution approach for improving the quality of image denoising filter. Quantitative validation was carried out on synthetic datasets generated with the BrainWeb simulator. The results show that our NL-means filter with wavelet subbands mixing outperforms the classical implementation of the NL-means filter in terms of denoising quality and computation time. Comparison with wellestablished methods, such as nonlinear diffusion filter and total variation minimization, shows that the proposed NL-means filter produces better denoising results. Finally, qualitative results on real data are presented

Unsupervised Domain Adaptation with Optimal Transport in multi-site segmentation of Multiple Sclerosis lesions from MRI data: Preprint

Automatic segmentation of Multiple Sclerosis (MS) lesions from Magnetic Resonance Imaging (MRI) images is essential for clinical assessment and treatment planning of MS. Recent years have seen an increasing use of Convolutional Neural Networks (CNNs) for this task. Although these methods provide accurate segmentation, their applicability in clinical settings remains limited due to a reproducibility issue across different image domains. MS images can have highly variable characteristics across patients, MRI scanners and imaging protocols; retraining a supervised model with data from each new domain is not a feasible solution because it requires manual annotation from expert radiologists. In this work, we explore an unsupervised solution to the problem of domain shift. We present a framework, Seg-JDOT, which adapts a deep model so that samples from a source domain and samples from a target domain sharing similar representations will be similarly segmented. We evaluated the framework on a multi-site dataset, MICCAI 2016, and showed that the adaptation towards a target site can bring remarkable improvements in a model performance over standard training

Diffusion directions imaging (high resolution reconstruction of white matter fascicles from low angular resolution diffusion MRI)

L'objectif de cette thèse est de fournir une chaine de traitement complète pour la reconstruction des faisceaux de la matière blanche à partir d'images pondérées en diffusion caractérisées par une faible résolution angulaire. Cela implique (i) d'inférer en chaque voxel un modèle de diffusion à partir des images de diffusion et (ii) d'accomplir une ''tractographie", i.e., la reconstruction des faisceaux à partir de ces modèles locaux. Notre contribution en modélisation de la diffusion est une nouvelle distribution statistique dont les propriétés sont étudiées en détail. Nous modélisons le phénomène de diffusion par un mélange de telles distributions incluant un outil de sélection de modèle destiné à estimer le nombre de composantes du mélange. Nous montrons que le modèle peut être correctement estimé à partir d'images de diffusion ''single-shell" à faible résolution angulaire et qu'il fournit des biomarqueurs spécifiques pour l'étude des tumeurs. Notre contribution en tractographie est un algorithme qui approxime la distribution des faisceaux émanant d'un voxel donné. Pour cela, nous élaborons un filtre particulaire mieux adapté aux distributions multi-modales que les filtres traditionnels. Pour démontrer l'applicabilité de nos outils en usage clinique, nous avons participé aux trois éditions du MICCAI DTI Tractography challenge visant à reconstruire le faisceau cortico-spinal à partir d'images de diffusion ''single-shell" à faibles résolutions angulaire et spatiale. Les résultats montrent que nos outils permettent de reconstruire toute l'étendue de ce faisceau.The objective of this thesis is to provide a complete pipeline that achieves an accurate reconstruction of the white matter fascicles using clinical diffusion images characterized by a low angular resolution. This involves (i) a diffusion model inferred in each voxel from the diffusion images and (ii) a tractography algorithm fed with these local models to perform the actual reconstruction of fascicles. Our contribution in diffusion modeling is a new statistical distribution, the properties of which are extensively studied. We model the diffusion as a mixture of such distributions, for which we design a model selection tool that estimates the number of mixture components. We show that the model can be accurately estimated from single shell low angular resolution diffusion images and that it provides specific biomarkers for studying tumors. Our contribution in tractography is an algorithm that approximates the distribution of fascicles emanating from a seed voxel. We achieve that by means of a particle filter better adapted to multi-modal distributions than the traditional filters. To demonstrate the clinical applicability of our tools, we participated to all three editions of the MICCAI DTI Tractography challenge aiming at reconstructing the cortico-spinal tract from single-shell low angular and low spatial resolution diffusion images. Results show that our pipeline provides a reconstruction of the full extent of the CST.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF

État de l'art des méthodes de correction des déformations cérébrales per-opératoires

L'utilisation croissante de systèmes de navigation pour l'aide à la chirurgie a permis de faciliter les interventions ainsi que la planification des gestes chirurgicaux. Néanmoins, dans le cas de la neurochirurgie où le geste opératoire doit être très précis, les systèmes actuels sont limités à cause de déformations per-opératoires nommées ``Brain Shift''. Le terme de 'Brain Shift' traduit le mouvement des structures cérébrales arrivant après ouverture de la boite crânienne (jusqu'à 25mm). Le recalage rigide réalisé par le système de neuronavigation entre les examens préopératoires et la position du patient en salle d'opération est donc entaché d'une imprécision. Ainsi, les informations fournies par le système de navigation deviennent partiellement obsolètes. Ce document propose une présentation des différentes techniques de mesure et de compensation du 'Brain Shift'. Les avantages et inconvénients de chaque approche seront soulignés avant de conclure par une brève présentation des méthodes de validation existantes. / Navigation systems become a very attractive tool in surgical planning and procedure. However, the accuracy and usefulness of such systems is limited in presence of soft-tissue deformations. In neurosurgery, this phenomenon is called ``Brain Shift''. The ``Brain shift'' is the motion of cerebral structures occurring after the craniotomy (up to 25mm). The neuronavigation system matches rigidly the pre-operative images with the surgical field. The hypothesis of a rigid registration is no longer valid because of deformations. This document presents a survey with classification of published methods to measure and compensate for the brain shift. The various validation framework are also presented

Integration of Probabilistic Atlas and Graph Cuts for Automated Segmentation of Multiple Sclerosis lesions

International audienceWe propose a framework for automated segmentation of Multiple Sclerosis (MS) lesions from MR brain images. It integrates a priori tissues and MS lesions information into a GraphCuts algorithm for improved segmentation results

Shanoir: Software as a Service Environment to Manage Population Imaging Research Repositories

International audienceSome of the major concerns of researchers and clinicians involved in popu- lation imaging experiments are on one hand, to manage the huge quantity and diversi- ty of produced data and, on the other hand, to be able to confront their experiments and the programs they develop with peers. In this context, we introduce Shanoir, a “Software as a Service” (SaaS) environment that offers cloud services for managing the information related to population imaging data production in the context of clini- cal neurosciences. We show how the produced images are accessible through the Sha- noir Data Management System, and we describe some of the data repositories that are hosted and managed by the Shanoir environment in different contexts