An Optimized PatchMatch for multi-scale and multi-feature label fusion

Automatic segmentation methods are important tools for quantitative analysis of Magnetic Resonance Images (MRI). Recently, patch-based label fusion approaches have demonstrated state-of-the-art segmentation accuracy. In this paper, we introduce a new patch-based label fusion framework to perform segmentation of anatomical structures. The proposed approach uses an Optimized PAtchMatch Label fusion (OPAL) strategy that drastically reduces the computation time required for the search of similar patches. The reduced computation time of OPAL opens the way for new strategies and facilitates processing on large databases. In this paper, we investigate new perspectives offered by OPAL, by introducing a new multi-scale and multi-feature framework. During our validation on hippocampus segmentation we use two datasets: young adults in the ICBM cohort and elderly adults in the EADC-ADNI dataset. For both, OPAL is compared to state-of-the-art methods. Results show that OPAL obtained the highest median Dice coefficient (89.9% for ICBM and 90.1% for EADC-ADNI). Moreover, in both cases, OPAL produced a segmentation accuracy similar to inter-expert variability. On the EADC-ADNI dataset, we compare the hippocampal volumes obtained by manual and automatic segmentation. The volumes appear to be highly correlated that enables to perform more accurate separation of pathological populations. (C) 2015 Elsevier Inc. All rights reserved.This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57). We also thank Tong Tong and Daniel Rueckert for providing us complete results of the methods proposed in Tong et al. (2013), Sonia Tangaro and Marina Boccardi for providing us complete results of the method proposed in Tangaro et al. (2014), and Katherine Gray and Robin Wolz for providing us complete results of the LEAP method proposed in Gray et al. (2014). Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). The ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics NV, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. Hoffmann-La Roche, Schering-Plough, Synarc Inc., as well as nonprofit partners, the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to the ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study was coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by the Spanish grant TIN2013-43457-R from the Ministerio de Economia y competitividad, NIH grants P30AG010129, K01 AG030514 and the Dana Foundation.Giraud, R.; Ta, V.; Papadakis, N.; Manjón Herrera, JV.; Collins, L.; Coupé Pierrick; Alzheimers Dis Neuroimaging Initia (2016). An Optimized PatchMatch for multi-scale and multi-feature label fusion. NeuroImage. 124(1):770-782. https://doi.org/10.1016/j.neuroimage.2015.07.076S770782124

Experimental study of water vaporization occurring inside the channel of a smooth-plate type heat exchanger connected to an adsorber and comparison with trends observed in absorption configuration

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Etude des mécanismes de vaporisation de l’eau à basse pression : application à un échangeur à plaques au cours d’un cycle d’adsorption. Congès Société Française de Thermique.

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SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches

International audienceSuperpixels have become very popular in many computer vision applications. Nevertheless, they remain underexploited since the superpixel decomposition may produce irregular and non stable segmentation results due to the dependency to the image content. In this paper, we first introduce a novel structure, a superpixel-based patch, called SuperPatch. The proposed structure, based on superpixel neighborhood, leads to a robust descriptor since spatial information is naturally included. The generalization of the PatchMatch method to SuperPatches, named SuperPatchMatch, is introduced. Finally, we propose a framework to perform fast segmentation and labeling from an image database, and demonstrate the potential of our approach since we outperform, in terms of computational cost and accuracy, the results of state-of-the-art methods on both face labeling and medical image segmentation

SuperPatchMatch : Un algorithme de correspondances robustes de patchs de superpixels

Les superpixels sont devenus très populaires dans de nombreuses applications de vision par ordinateur. Néanmoins, ils restent sous-exploités du fait de l'irrégularité des décompositions qui diffèrent selon les images. Dans ce travail, nous introduisons d'abord une nouvelle structure, un patch de superpixels, appelée SuperPatch. La structure proposée, basée sur le voisinage du superpixel, définit un descripteur robuste incluant les relations spatiales entre superpixels voisins. La généralisation de la méthode de recherche de correspondance PatchMatch aux SuperPatchs, nommée SuperPatchMatch, est alors introduite. Enfin, nous proposons une adaptation de la méthodè a l'étiquetage automatique depuis une bibliothèque d'images d'exemples. Nous démontrons alors le potentiel de notre approche en obtenant des résultats supérieurs à ceux d'approches basées apprentissages, sur des expériences d'étiquetage de visages.Generalized Optimal Transport Models for Image processin

Optimisation de l'algorithme PatchMatch pour la segmentation de structures anatomiques

– Les méthodes de segmentation automatique sont des outils importants pour l'analyse des images par résonance magnétique. Dans ce travail, nous introduisons une nouvelle méthode basée sur l'utilisation de patchs pour effectuer une segmentation de structures anatomiques. La méthode proposée est basée sur l'algorithme PatchMatch, optimisé pour la fusion d'étiquettes. Elle est nommée OPAL (pour Optimized PAtchMatch Label fusion), et fournit une précision de segmentation très compétitive en quasi temps-réel (moins d'1s de traitement par sujet). – Automatic segmentation methods are important tools for analysis of magnetic resonance images. In this paper, we introduce a new patch-based method using the PatchMatch algorithm to perform the segmentation of anatomical structures. Based on an Optimized PAtchMatch Label fusion (OPAL) strategy, the proposed method provides very competitive segmentation accuracy in near real-time (less than 1s per subject)