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MICCAI
2010
Springer

Nonlocal Patch-Based Label Fusion for Hippocampus Segmentation

10 years 8 months ago
Nonlocal Patch-Based Label Fusion for Hippocampus Segmentation
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.
Pierrick Coupé, José V. Manjó
Added 14 Feb 2011
Updated 14 Feb 2011
Type Journal
Year 2010
Where MICCAI
Authors Pierrick Coupé, José V. Manjón, Vladimir Fonov, Jens C. Pruessner, Montserrat Robles, D. Louis Collins
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