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

• 综述 • 上一篇    下一篇

多图谱方法在脑MR图像分割中的应用

孙亮1,张丽2,张道强1,*()   

  1. 1. 南京航空航天大学 计算机科学与技术学院 模式分析与机器智能工业和信息化部重点实验室,南京 211106
    2. 南京医科大学附属脑科医院 老年医学科,南京 210029
  • 收稿日期:2019-02-27 接受日期:2019-04-28 出版日期:2019-05-14 发布日期:2019-05-16
  • 通讯作者: 张道强 E-mail:dqzhang@nuaa.edu.cn

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 Online:2019-05-14 Published:2019-05-16
  • Contact: Zhang Daoqiang E-mail:dqzhang@nuaa.edu.cn

摘要:

脑感兴趣区域分割是很多脑疾病计算机辅助分析的重要步骤。然而人类大脑具有复杂的解剖结构,同时脑磁共振(Magnetic Resonance, MR)图像处理通常会面临感兴趣区域灰度对比度低,个体之间和个体内的差异性等问题。为了解决这些问题,近年来,基于多图谱的很多方法被用于脑感兴趣区域的分割。在这篇综述中,我们对一些基于多图谱的脑MR图像分割方法进行系统的介绍,包括常用的配准工具箱,经典的标签融合方法,常用数据集以及多图谱分割在临床研究中的应用。我们认为,将图谱图像的解剖结构先验信息融入端到端的深度学习框架中用于脑图像感兴趣区域分割将是多图谱方法未来一个重要的研究方向。

关键词: 多图谱, 脑, 分割, 磁共振

Abstract:

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

基金资助: 国家自然科学基金(Nos. 61876082, 61861130366, 61703301),江苏省333高层次人才培养工程

Copyright © 2018 Chinese Academy of Medical Sciences. All right reserved.
 
www.cmsj.cams.cn
京公安备110402430088 京ICP备06002729号-1  Powered by Magtech.

Supervised by National Health & Family Plan Commission of PRC

9 Dongdan Santiao, Dongcheng district, Beijing, 100730 China

Tel: 86-10-65105897  Fax:86-10-65133074 

E-mail: cmsj@cams.cn  www.cmsj.cams.cn

Copyright © 2018 Chinese Academy of Medical Sciences

All right reserved.

京公安备110402430088  京ICP备06002729号-1