Optimized patchMatch for near real time and accurate label fusion

Automatic segmentation methods are important tools for quantitative analysis of magnetic resonance images. Recently, patch-based label fusion approaches demonstrated state-of-the-art segmentation accuracy. In this paper, we introduce a new patch-based method using the PatchMatch algorithm to perform...

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Published inMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Vol. 17; no. Pt 3; p. 105
Main Authors Ta, Vinh-Thong, Giraud, Rémi, Collins, D Louis, Coupé, Pierrick
Format Journal Article
LanguageEnglish
Published Germany 2014
Subjects
Algorithms
Computer Systems
Hippocampus - anatomy & histology
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Magnetic Resonance Imaging - methods
Pattern Recognition, Automated - methods
Reference Values
Reproducibility of Results
Sensitivity and Specificity
Software
Subtraction Technique
Online AccessGet more information

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Summary:Automatic segmentation methods are important tools for quantitative analysis of magnetic resonance images. Recently, patch-based label fusion approaches demonstrated state-of-the-art segmentation 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 accuracy in near real time. During our validation on hippocampus segmentation 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.