Regional brain development analysis through registration using anisotropic similarity, a constrained affine transformation

We propose a novel method to quantify brain growth in 3 arbitrary orthogonal directions of the brain or its sub-regions through linear registration. This is achieved by introducing a 9 degrees of freedom (dof) transformation called anisotropic similarity which is an affine transformation with constrained scaling directions along arbitrarily chosen orthogonal vectors. This gives the opportunity to extract scaling factors describing brain growth along those directions by registering a database of subjects onto a common reference. This information about directional growth brings insights that are not usually available in longitudinal volumetric analysis. The interest of this method is illustrated by studying the anisotropic regional and global brain development of 308 healthy subjects betwen 0 and 19 years old. A gender comparison of those scaling factors is also performed for four age-intervals. We demonstrate through these applications the stability of the method to the chosen reference and its ability to highlight growth differences accros regions and gender

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

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

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

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

Robust Fusion of Probability Maps

International audienceThe fusion of probability maps is required when trying to analyse a collection of image labels or probability maps produced by several segmentation algorithms or human raters. The challenge is to weight properly the combination of maps in order to reflect the agreement among raters, the presence of outliers and the spatial uncertainty in the consensus. In this paper, we address several shortcomings of prior work in continuous label fusion. We introduce a novel approach to jointly estimate a reliable consensus map and assess the production of outliers and the confidence in each rater. Our probabilistic model is based on Student's t-distributions allowing local estimates of raters' performances. The introduction of bias and spatial priors leads to proper rater bias estimates and a control over the smoothness of the consensus map. Image intensity information is incorporated by geodesic distance transform for binary masks. Finally, we propose an approach to cluster raters based on variational boosting thus producing possibly several alternative consensus maps. Our approach was successfully tested on the MICCAI 2016 MS lesions dataset, on MR prostate delineations and on deep learning based segmentation predictions of lung nodules from the LIDC dataset

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

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

Test-time Unsupervised Domain Adaptation

Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain). This motivates the active field of domain adaptation. While some approaches to the problem require labeled data from the target domain, others adopt an unsupervised approach to domain adaptation (UDA). Evaluating UDA methods consists of measuring the model's ability to generalise to unseen data in the target domain. In this work, we argue that this is not as useful as adapting to the test set directly. We therefore propose an evaluation framework where we perform test-time UDA on each subject separately. We show that models adapted to a specific target subject from the target domain outperform a domain adaptation method which has seen more data of the target domain but not this specific target subject. This result supports the thesis that unsupervised domain adaptation should be used at test-time, even if only using a single target-domain subjectComment: Accepted at MICCAI 202