文献类型：期刊文献

英文题名：Joint Neighboring Coding with a Low-Rank Constraint for Multi-Atlas Based Image Segmentation

作者：Zhu, Hancan[1];He, Guanghua[2]

机构：[1]Shaoxing Univ, Sch Math Phys & Informat, Shaoxing 312000, Peoples R China;[2]Zhejiang Yuexiu Univ Foreign Languages, Sch Int Business, Shaoxing 312000, Peoples R China

年份：2020

卷号：10

期号：2

起止页码：310

外文期刊名：JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS

收录：SCI-EXPANDED(收录号：WOS:000496931800005)、、WOS

基金：This work was supported in part by National Natural Science Foundation of China (Nos. 61602307, 61877039), and Natural Science Foundation of Zhejiang Province (No. LY19F020013).

语种：英文

外文关键词：Multi-Atlas Segmentation; Label Fusion; Joint Coding Method; Low-Rank Constraint

外文摘要：Multi-atlas methods have been successful for solving many medical image segmentation problems. Under the multi-atlas segmentation framework, labels of atlases are first propagated to the target image space with the deformation fields generated by registering atlas images onto a target image, and then these labels are fused to obtain the final segmentation. While many label fusion strategies have been developed, weighting based label fusion methods have attracted considerable attention. In this paper, we first present a unified framework for weighting based label fusion methods. Under this unified framework, we find that most of recent developed weighting based label fusion methods jointly consider the pair-wise dependency between atlases. However, they independently label voxels to be segmented, ignoring their neighboring spatial structure that might be informative for obtaining robust segmentation results for noisy images. Taking into consideration of potential correlation among neighboring voxels to be segmented, we propose a joint coding method (JCM) with a low-rank constraint for the multi-atlas based image segmentation in a general framework that unifies existing weighting based label fusion methods. The method has been validated for segmenting hippocampus from MR images. It is demonstrated that our method can achieve competitive segmentation performance as the state-of-the-art methods, especially when the quality of images is poor.