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.
hippocampus segmentation nonlocal means estimator
Pierrick Coupé José V.Manjón Vladimir Fonov Jens Pruessner Montserrat Robles D.Louis Collins
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University,Montreal, Canada. Instituto de Aplicaciones de las Tecnologías de la Información y de las ComunicacionesAvanzadas (ITA McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
国际会议
北京
英文
129–136
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）