Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation.
作者： Pierrick Coupé , José V. Manjón , Vladimir Fonov , Jens Pruessner , Montserrat Robles
DOI: 10.1016/J.NEUROIMAGE.2010.09.018
关键词: Segmentation 、 Computer science 、 Ventricle 、 Lateral ventricles 、 Computer vision 、 Scale-space segmentation 、 Prior probability 、 Segmentation-based object categorization 、 Image processing 、 Artificial intelligence 、 Image segmentation
摘要: 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 In this study, we propose novel patch-based method using expert manual segmentations as priors to achieve task. Inspired by recent work image denoising, the proposed nonlocal produces accurate robust segmentation. Validation with two different datasets is presented. our experiments, hippocampi 80 healthy subjects lateral ventricles patients Alzheimer's disease were segmented. The influence on segmentation parameters such patch size number training was also studied. A comparison an appearance-based template-based carried out. highest median kappa index values obtained 0.884 for hippocampus 0.959 ventricle
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