Non-central chi estimation of multi-compartment models improves model selection by reducing overfitting

International audienceDiffusion images are known to be corrupted with a non-central chi (NCC)-distributed noise [1]. There has been a number of proposed image denoising methods that account for this particular noise distribution [1,2,3]. However, to the best of our knowledge, no study was performed to assess the influence of the noise model in the context of diffusion model estimation as was suggested in [4]. In particular, multi-compartment models [5] are an appealing class of models to describe the white matter microstructure but require the optimal number of compartments to be known a priori. Its estimation is no easy task since more complex models will always better fit the data, which is known as over-fitting. However, MCM estimation in the literature is performed assuming a Gaussian-distributed noise [5,6]. In this preliminary study, we aim at showing that using the appropriate NCC distribution for modelling the noise model reduces significantly the over-fitting, which could be helpful for unravelling model selection issues and obtaining better model parameter estimates

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

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

Statistics on Diffeomorphisms in a Log-Euclidean Framework

International audienceIn this article, we focus on the computation of statistics of invertible geometrical deformations (i.e., diffeomorphisms), based on the generalization to this type of data of the notion of principal logarithm. Remarkably, this logarithm is a simple 3D vector field, and can be used for diffeomorphisms close enough to the identity. This allows to perform vectorial statistics on diffeomorphisms, while preserving the invertibility constraint, contrary to Euclidean statistics on displacement fields

MSSEG-2 challenge proceedings: Multiple sclerosis new lesions segmentation challenge using a data management and processing infrastructure

International audienceThis proceedings book gathers methodological papers describing the segmenta-tion methods evaluated at the second MICCAI Challenge on Multiple Sclerosisnew lesions segmentation challenge using a data management and processinginfrastructure. This challenge took place as part of an effort of the OFSEP1(French registry on multiple sclerosis aiming at gathering, for research purposes,imaging data, clinical data and biological samples from the French populationof multiple sclerosis subjects) and FLI2(France Life Imaging, devoted to setupa national distributed e-infrastructure to manage and process medical imagingdata). These joint efforts are directed towards automatic segmentation of MRIscans of MS patients to help clinicians in their daily practice. This challengetook place at the MICCAI 2021 conference, on September 23rd 2021.More precisely, the problem addressed in this challenge is as follows. Con-ventional MRI is widely used for disease diagnosis, patient follow-up, monitoringof therapies, and more generally for the understanding of the natural history ofMS. A growing literature is interested in the delineation of new MS lesions onT2/FLAIR by comparing one time point to another. This marker is even morecrucial than the total number and volume of lesions as the accumulation of newlesions allows clinicians to know if a given anti-inflammatory DMD (disease mod-ifying drug) works for the patient. The only indicator of drug efficacy is indeedthe absence of new T2 lesions within the central nervous system. Performingthis new lesions count by hand is however a very complex and time consumingtask. Automating the detection of these new lesions would therefore be a majoradvance for evaluating the patient disease activity.Based on the success of the first MSSEG challenge, we have organized aMICCAI sponsored online challenge, this time on new MS lesions detection3.This challenge has allowed to 1) estimate the progress performed during the2016 - 2021 period, 2) extend the number of patients, and 3) focus on the newlesions crucial clinical marker. We have performed the evaluation task on a largedatabase (100 patients, each with two time points) compiled from the OFSEPcohort with 3D FLAIR images from different centers and scanners. As in ourprevious challenge, we have conducted the evaluation on a dedicated platform(FLI-IAM) to automate the evaluation and remove the potential biases due tochallengers seeing the images on which the evaluation is made

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

A Fast and Log-Euclidean Polyaffine Framework for Locally Affine Registration

Projet ASCLEPIOSIn this article, we focus on the parameterization of non-rigid geometrical deformations with a small number of flexible degrees of freedom . In previous work, we proposed a general framework called polyaffine to parameterize deformations with a finite number of rigid or affine components, while guaranteeing the invertibility of global deformations. However, this framework lacks some important properties: the inverse of a polyaffine transformation is not polyaffine in general, and the polyaffine fusion of affine components is not invariant with respect to a change of coordinate system. We present here a novel general framework, called Log-Euclidean polyaffine, which overcomes these defects. We also detail a simple algorithm, the Fast Polyaffine Transform, which allows to compute very efficiently Log-Euclidean polyaffine transformations and their inverses on regular grids. The results presented here on real 3D locally affine registration suggest that our novel framework provides a general and efficient way of fusing local rigid or affine deformations into a global invertible transformation without introducing artifacts, independently of the way local deformations are first estimated. Last but not least, we show in this article that the Log-Euclidean polyaffine framework is implicitely based on a Log-Euclidean framework for rigid and affine transformations, which generalizes to linear transformations the Log-Euclidean framework recently proposed for tensors. We detail in the Appendix of this article the properties of this novel framework, which allows a straightforward and efficient generalization to linear transformations of classical vectorial tools, with excellent theoretical properties. In particular, we propose here a simple generalization to locally rigid or affine deformations of a visco-elastic regularization energy used for dense transformations

The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery: The DTI Challenge on Tractography for Neurosurgery

Diffusion tensor imaging tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography-derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop

Création et utilisation d'atlas anatomiques numériques pour la radiothérapie

The main objective of this thesis is to provide radio-oncology specialists with automatic tools for delineating organs at risk of a patient undergoing a radiotherapy treatment of cerebral or head and neck tumors.To achieve this goal, we use an anatomical atlas, i.e. a representative anatomy associated to a clinical image representing it. The registration of this atlas allows to segment automatically the patient structures and to accelerate this process. Contributions in this method are presented on three axes.First, we want to obtain a registration method which is as independent as possible w.r.t. the setting of its parameters. This setting, done by the clinician, indeed needs to be minimal while guaranteeing a robust result. We therefore propose registration methods allowing to better control the obtained transformation, using outlier rejection techniques or locally affine transformations.The second axis is dedicated to the consideration of structures associated with the presence of the tumor. These structures, not present in the atlas, indeed lead to local errors in the atlas-based segmentation. We therefore propose methods to delineate these structures and take them into account in the registration.Finally, we present the construction of an anatomical atlas of the head and neck region and its evaluation on a database of patients. We show in this part the feasibility of the use of an atlas for this region, as well as a simple method to evaluate the registration methods used to build an atlas.All this research work has been implemented in a commercial software (Imago from DOSIsoft), allowing us to validate our results in clinical conditions.L'objectif de cette thèse est de fournir aux radiothérapeutes des outils de contourage automatique des structures à risque pour la planification de la radiothérapie des tumeurs cérébrales et de la région ORL.Nous utilisons pour cela un atlas anatomique, constitué d'une représentation de l'anatomie associée à une image de celle-ci. Le recalage de cet atlas permet de contourer automatiquement les organes du patient et ainsi obtenir un gain de temps considérable. Les contributions présentées se concentrent sur trois axes.Tout d'abord, nous souhaitons obtenir une méthode de recalage la plus indépendante possible du réglage de ses paramètres. Celui-ci, effectué par le médecin, se doit d'être minimal, tout en garantissant un résultat robuste. Nous proposons donc des méthodes de recalage permettant un meilleur contrôle de la transformation obtenue, en passant par des techniques de rejet d'appariements aberrants ou en utilisant des transformations localement affines.Le second axe est consacré à la prise en compte de structures dues à la tumeur. En effet, la présence de ces structures, absentes de l'atlas, perturbe le recalage de celui-ci. Nous proposons donc également des méthodes afin de contourer ces structures et de les prendre en compte dans le recalage.Enfin, nous présentons la construction d'un atlas ORL et son évaluation sur une base de patients. Nous montrons ici la faisabilité de l'utilisation d'un atlas de cette région, ainsi qu'une méthode simple afin d'évaluer les méthodes de recalage utilisées pour construire un atlas.L'ensemble de ces travaux a été implémenté dans le logiciel Imago de DOSIsoft, ceci ayant permis d'effectuer une validation en conditions cliniques

Création et utilisation d'atlas anatomiques numériques pour la radiothérapie

The main objective of this thesis is to provide radio-oncology specialists with automatic tools for delineating organs at risk of a patient undergoing a radiotherapy treatment of cerebral or head and neck tumors.To achieve this goal, we use an anatomical atlas, i.e. a representative anatomy associated to a clinical image representing it. The registration of this atlas allows to segment automatically the patient structures and to accelerate this process. Contributions in this method are presented on three axes.First, we want to obtain a registration method which is as independent as possible w.r.t. the setting of its parameters. This setting, done by the clinician, indeed needs to be minimal while guaranteeing a robust result. We therefore propose registration methods allowing to better control the obtained transformation, using outlier rejection techniques or locally affine transformations.The second axis is dedicated to the consideration of structures associated with the presence of the tumor. These structures, not present in the atlas, indeed lead to local errors in the atlas-based segmentation. We therefore propose methods to delineate these structures and take them into account in the registration.Finally, we present the construction of an anatomical atlas of the head and neck region and its evaluation on a database of patients. We show in this part the feasibility of the use of an atlas for this region, as well as a simple method to evaluate the registration methods used to build an atlas.All this research work has been implemented in a commercial software (Imago from DOSIsoft), allowing us to validate our results in clinical conditions.L'objectif de cette thèse est de fournir aux radiothérapeutes des outils de contourage automatique des structures à risque pour la planification de la radiothérapie des tumeurs cérébrales et de la région ORL.Nous utilisons pour cela un atlas anatomique, constitué d'une représentation de l'anatomie associée à une image de celle-ci. Le recalage de cet atlas permet de contourer automatiquement les organes du patient et ainsi obtenir un gain de temps considérable. Les contributions présentées se concentrent sur trois axes.Tout d'abord, nous souhaitons obtenir une méthode de recalage la plus indépendante possible du réglage de ses paramètres. Celui-ci, effectué par le médecin, se doit d'être minimal, tout en garantissant un résultat robuste. Nous proposons donc des méthodes de recalage permettant un meilleur contrôle de la transformation obtenue, en passant par des techniques de rejet d'appariements aberrants ou en utilisant des transformations localement affines.Le second axe est consacré à la prise en compte de structures dues à la tumeur. En effet, la présence de ces structures, absentes de l'atlas, perturbe le recalage de celui-ci. Nous proposons donc également des méthodes afin de contourer ces structures et de les prendre en compte dans le recalage.Enfin, nous présentons la construction d'un atlas ORL et son évaluation sur une base de patients. Nous montrons ici la faisabilité de l'utilisation d'un atlas de cette région, ainsi qu'une méthode simple afin d'évaluer les méthodes de recalage utilisées pour construire un atlas.L'ensemble de ces travaux a été implémenté dans le logiciel Imago de DOSIsoft, ceci ayant permis d'effectuer une validation en conditions cliniques