Nonlocal Patch-Based Label Fusion for Hippocampus Segmentation

Abstract : Quantitative magnetic resonance analysis often requires accurate, robust and reliable automatic extraction of anatomical structures. Recently, template-warping methods incorporating a label fusion strategy have demonstrated high accuracy in segmenting cerebral structures. In this study, we propose a novel patch-based method using expert segmentation priors to achieve this task. Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation. During our experiments, the hippocampi of 80 healthy subjects were segmented. The influence on segmentation accuracy of different parameters such as patch size or number of training subjects was also studied. Moreover, a comparison with an appearance-based method and a template-based method was carried out. The highest median kappa value obtained with the proposed method was 0.884, which is competitive compared with recently published methods.
Type de document :
Communication dans un congrès
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, Sep 2010, Beijing, China. 6363, pp.129-136, 2010, 〈10.1007/978-3-642-15711-0_17〉
Liste complète des métadonnées

Littérature citée [17 références]  Voir  Masquer  Télécharger

http://www.hal.inserm.fr/inserm-00524011
Contributeur : Pierrick Coupé <>
Soumis le : mercredi 6 octobre 2010 - 21:14:26
Dernière modification le : vendredi 13 avril 2018 - 19:54:13
Document(s) archivé(s) le : vendredi 7 janvier 2011 - 03:03:16

Fichier

MICCAI2010-Camera-Ready.pdf
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité - Pas d'utilisation commerciale - Pas de modification 4.0 International License

Identifiants

Collections

Citation

Pierrick Coupé, Jose Vicente Manjon, Vladimir Fonov, Jens Pruessner, Montserrat Robles, et al.. Nonlocal Patch-Based Label Fusion for Hippocampus Segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, Sep 2010, Beijing, China. 6363, pp.129-136, 2010, 〈10.1007/978-3-642-15711-0_17〉. 〈inserm-00524011〉

Partager

Métriques

Consultations de la notice

256

Téléchargements de fichiers

425