Going deeper with brain morphometry using neural networks

Brain morphometry from magnetic resonance imaging (MRI) is a consolidated biomarker for many neurodegenerative diseases. Recent advances in this domain indicate that deep convolutional neural networks can infer morphometric measurements within a few seconds. Nevertheless, the accuracy of the devised model for insightful bio-markers (mean curvature and thickness) remains unsatisfactory. In this paper, we propose a more accurate and efficient neural network model for brain morphometry named HerstonNet. More specifically, we develop a 3D ResNet-based neural network to learn rich features directly from MRI, design a multi-scale regression scheme by predicting morphometric measures at feature maps of different resolutions, and leverage a robust optimization method to avoid poor quality minima and reduce the prediction variance. As a result, HerstonNet improves the existing approach by 24.30% in terms of intraclass correlation coefficient (agreement measure) to FreeSurfer silver-standards while maintaining a competitive run-time

SEGMENTATION: A DATA DRIVEN APPROACH THOUGH NEURAL NETWORK

International audienceImage segmentation is a field that has known huge breakthroughs this last decade especially with applications to autonomous cars. We propose to adapt a recent method Mask R-CNN[1] to segment images of biological cells. The images used in this work are provided by a fluorescence microscope which brings artifacts and non-uniform brightness. Classical segmentation methods fail to segment the cells satisfactorily; to overcome this problem we make use of a state of the art deep learning method. This method is trained on a very small dataset and provides both segmentation and confidence score. We then use the segmentation maps to produce segmentation and tracking on videos

Biolapse Toolbox

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3/4 discrete optimal transport

International audienceThis paper deals with the 3 4-discrete 2-Wasserstein optimal transport between two measures, where one is supported by a set of segment and the other one is supported by a set of Dirac masses. We select the most suitable optimization procedure that computes the optimal transport and provide numerical examples of approximation of cloud data by segments

Approximation of curves with piecewise constant or piecewise linear functions

In this paper we compute the Hausdorff distance between sets of continuous curves and sets of piecewise constant or linear discretizations. These sets are Sobolev balls given by the continuous or discrete L p-norm of the derivatives. We detail the suitable discretization or smoothing procedure which are preservative in the sense of these norms. Finally we exhibit the link between Eulerian numbers and the uniformly space knots B-spline used for smoothing

Differentiation and regularity of semi-discrete optimal transport with respect to the parameters of the discrete measure

International audienceThis paper aims at determining under which conditions the semi-discrete optimal transport is twice dierentiable with respect to the parameters of the discrete measure and exhibits numerical applications. The discussion focuses on minimal conditions on the background measure to ensure dierentiability. We provide numerical illustrations in stippling and blue noise problems

Approches variationnelles pour le stippling : distances L 2 ou transport optimal ?

International audienceLe stippling est un problème qui a beaucoup progressé dernièrement grâce à l'introduction de méthodes variationnelles. On s'intéresse ici à deux types de formulations. L'une repose sur une distance L 2 entre mesures et fait appel à des outils d'analyse harmonique appliquée. L'autre repose sur la distance de Wasserstein et fait appel à des outils de géométrie algorithmique. Différentes méthodes de résolution et de discrétisation sont comparées et nous présentons leurs atouts et leurs limitations. Abstract-Stippling is a problem that recently found elegant and efficient solutions thanks to the introduction of variational methods. The aim of this paper is to compare two state-of-the-art approaches: one is based on the minimization of an L 2 norm (which links to applied harmonic analysis), while the other is based on the Wasserstein distance (which links to computational geometry)

Optimal Transport Approximation of 2-Dimensional Measures

International audienceWe propose a fast and scalable algorithm to project a given density on a set of structured measures defined over a compact 2D domain. The measures can be discrete or supported on curves for instance. The proposed principle and algorithm are a natural generalization of previous results revolving around the generation of blue-noise point distributions, such as Lloyd's algorithm or more advanced techniques based on power diagrams. We analyze the convergence properties and propose new approaches to accelerate the generation of point distributions. We also design new algorithms to project curves onto spaces of curves with bounded length and curvature or speed and acceleration. We illustrate the algorithm's interest through applications in advanced sampling theory, non-photorealistic rendering and path planning

CorticalFlow++ : Boosting Cortical Surface Reconstruction Accuracy, Regularity, and Interoperability

The problem of Cortical Surface Reconstruction from magnetic resonance imaging has been traditionally addressed using lengthy pipelines of image processing techniques like FreeSurfer, CAT, or CIVET. These frameworks require very long runtimes deemed unfeasible for real-time applications and unpractical for large-scale studies. Recently, supervised deep learning approaches have been introduced to speed up this task cutting down the reconstruction time from hours to seconds. Using the state-of-the-art CorticalFlow model as a blueprint, this paper proposes three modifications to improve its accuracy and interoperability with existing surface analysis tools, while not sacrificing its fast inference time and low GPU memory consumption. First, we employ a more accurate ODE solver to reduce the diffeomorphic mapping approximation error. Second, we devise a routine to produce smoother template meshes avoiding mesh artifacts caused by sharp edges in CorticalFlow’s convex-hull based template. Last, we recast pial surface prediction as the deformation of the predicted white surface leading to a one-to-one mapping between white and pial surface vertices. This mapping is essential to many existing surface analysis tools for cortical morphometry. We name the resulting method CorticalFlow+ +. Using large-scale datasets, we demonstrate the proposed changes provide more geometric accuracy and surface regularity while keeping the reconstruction time and GPU memory requirements almost unchanged.</p