多图谱方法在脑MR图像分割中的应用

孙亮; 张丽; 张道强

doi:10.24920/003576

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多图谱方法在脑MR图像分割中的应用

中国医学科学杂志（英文版） 2019年34卷第2期页码：110-119

Review | Updated：2024-04-10

- 多图谱方法在脑MR图像分割中的应用
- Affiliations：
  
  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
- Author bio：
- Funds：
  
  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Supported by the National Natural Science Foundation of China (Nos. 61876082, 61861130366, 61703301) and the Jiangsu Provincial 333 High-level Talent Cultivation Projects.
- DOI：10.24920/003576
  中图分类号：
- 收稿日期：2019-02-27，
  
  录用日期：2019-4-28，
  
  网络出版日期：2019-05-14，
  
  纸质出版日期：2019-05-14
- Accepted：
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孙亮, 张丽, 张道强. 多图谱方法在脑MR图像分割中的应用[J]. 中国医学科学杂志（英文版）, 2019,34(2):110-119.

Sun Liang, Zhang Li, Zhang Daoqiang. Multi-Atlas Based Methods in Brain MR Image Segmentation[J]. Chinese medical sciences journal;chinese medical sciences journal, 2019, 34(2): 110-119.
孙亮, 张丽, 张道强. 多图谱方法在脑MR图像分割中的应用[J]. 中国医学科学杂志（英文版）, 2019,34(2):110-119. DOI： 10.24920/003576.

Sun Liang, Zhang Li, Zhang Daoqiang. Multi-Atlas Based Methods in Brain MR Image Segmentation[J]. Chinese medical sciences journal;chinese medical sciences journal, 2019, 34(2): 110-119. DOI： 10.24920/003576.

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。

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.

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