Optimized PatchMatch for Near Real Time and Accurate Label Fusion

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.
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Communication dans un congrès
MICCAI 2014, Sep 2014, United States. 8 p., 2014
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Contributeur : Vinh-Thong Ta <>
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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〉

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