Chinese Medical Sciences Journal ›› 2019, Vol. 34 ›› Issue (2): 110-119.doi: 10.24920/003576

• Review • Previous Articles     Next Articles

Multi-Atlas Based Methods in Brain MR Image Segmentation

Sun Liang1, Zhang Li2, Zhang Daoqiang1, *()   

  1. 1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China
    2. Department of Geriatrics, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
  • Received:2019-02-27 Accepted:2019-04-28 Published:2019-05-14 Online:2019-05-16
  • Contact: Zhang Daoqiang E-mail:dqzhang@nuaa.edu.cn
Multi-atlas based methods for brain MR image segmentation were systematically reviewed in this review article. The author proposed that incorporating the anatomical prior into the end-to-end deep learning architectures, for brain ROI segmentation is an important direction in the future.

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.

Key words: multi-atlas, brain, segmentation, magnetic resonance

Funding: Supported by the National Natural Science Foundation of China (Nos. 61876082, 61861130366, 61703301) and the Jiangsu Provincial 333 High-level Talent Cultivation Projects.

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