McGill University (845, rue Sherbrooke O. Montréal (Québec) Canada H3A 0G4 - Canada)
Abstract : Automatic segmentation methods are important tools for quantitative analysis of Magnetic Resonance Images. Recently, patch- based label fusion approaches demonstrated state-of-the-art segmenta- tion accuracy. In this paper, we introduce a new patch-based method using the PatchMatch algorithm to perform segmentation of anatomical structures. Based on an Optimized PAtchMatch Label fusion (OPAL) strategy, the proposed method provides competitive segmentation accu- racy in near real time. During our validation on hippocampus segmenta- tion of 80 healthy subjects, OPAL was compared to several state-of-the- art methods. Results show that OPAL obtained the highest median Dice coefficient (89.3%) in less than 1 sec per subject. These results highlight the excellent performance of OPAL in terms of computation time and segmentation accuracy compared to recently published methods.
https://hal.archives-ouvertes.fr/hal-01006329
Contributeur : Vinh-Thong Ta
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Soumis le : dimanche 15 juin 2014 - 14:50:33
Dernière modification le : jeudi 11 janvier 2018 - 06:20:17
Document(s) archivé(s) le : lundi 15 septembre 2014 - 10:36:43
Vinh-Thong Ta, Rémi Giraud, D. Louis Collins, Pierrick Coupé. Optimized PatchMatch for Near Real Time and Accurate Label Fusion. MICCAI 2014, Sep 2014, United States. 8 p., 2014. 〈hal-01006329〉
