Multi-Atlas Based Methods in Brain MR Image Segmentation

• Published in: Chinese medical sciences journal Vol. 34; no. 2; pp. 110 - 119
• Main Authors: Sun, Liang, Zhang, Li, Zhang, Daoqiang
• Format: Journal Article
• Language: English
• Published: China Elsevier B.V 30.06.2019; College of Computer Science and Technology, Nanjing University of Aeronautics and Astronau-tics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China%Department of Geriatrics, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
• Subjects: brain; Brain - diagnostic imaging; Brain Mapping - methods; Humans; magnetic resonance; Magnetic Resonance Imaging - methods; multi-atlas; segmentation; segmentation; magnetic resonance; multi-atlas; brain
• DOI: 10.24920/003576
• ISSN: 1001-9294

Brain region-of-interesting (ROI) segmentation is an important prerequisite step for many computer-aid brain disease analyses. However, the human brain has the complicated anatomical structure. Meanwhile, the brain MR images often suffer from the low intensity contrast around the boundary of ROIs, large inter-subject variance and large inner-subject variance. To address these issues, many multi-atlas based segmentation methods are proposed for brain ROI segmentation in the last decade. In this paper, multi-atlas based methods for brain MR image segmentation were reviewed regarding several registration toolboxes which are widely used in the multi-atlas methods, conventional methods for label fusion, datasets that have been used for evaluating the multi-atlas methods, as well as the applications of multi-atlas based segmentation in clinical researches. We propose that incorporating the anatomical prior into the end-to-end deep learning architectures for brain ROI segmentation is an important direction in the future.