文献类型：期刊文献

英文题名：Metric Learning for Multi-atlas based Segmentation of Hippocampus

作者：Zhu, Hancan[1];Cheng, Hewei[2];Yang, Xuesong[3];Fan, Yong[4]

机构：[1]Shaoxing Univ, Sch Math Phys & Informat, Shaoxing 312000, Peoples R China;[2]Chongqing Univ Posts & Telecommun, Sch Bioinformat, Dept Biomed Engn, Chongqing 400065, Peoples R China;[3]Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China;[4]Univ Penn, Dept Radiol, Perelman Sch Med, Philadelphia, PA 19104 USA

年份：2017

卷号：15

期号：1

起止页码：41

外文期刊名：NEUROINFORMATICS

收录：SCI-EXPANDED(收录号：WOS:000394260000005)、、Scopus(收录号：2-s2.0-84988421939)、WOS

基金：This work was supported in part by National Key Basic Research and Development Program (No. 2015CB856404), National Natural Science Foundation of China (No. 81271514, 61473296, 61602307), and NIH grants EB022573, CA189523, and AG014971.

语种：英文

外文关键词：Multi-atlas image segmentation; Hippocampus segmentation; Metric learning; Label fusion

外文摘要：Automatic and reliable segmentation of hippocampus from MR brain images is of great importance in studies of neurological diseases, such as epilepsy and Alzheimer's disease. In this paper, we proposed a novel metric learning method to fuse segmentation labels in multi-atlas based image segmentation. Different from current label fusion methods that typically adopt a predefined distance metric model to compute a similarity measure between image patches of atlas images and the image to be segmented, we learn a distance metric model from the atlases to keep image patches of the same structure close to each other while those of different structures are separated. The learned distance metric model is then used to compute the similarity measure between image patches in the label fusion. The proposed method has been validated for segmenting hippocampus based on the EADC-ADNI dataset with manually labelled hippocampus of 100 subjects. The experiment results demonstrated that our method achieved statistically significant improvement in segmentation accuracy, compared with state-of-the-art multi-atlas image segmentation methods.