Sun Liang, Zhang Li, Zhang Dao-Qiang
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 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.
Chin Med Sci J. 2019 Jun 30;34(2):110-119. doi: 10.24920/003576.
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.
脑感兴趣区域（ROI）分割是许多计算机辅助脑疾病分析的重要前提步骤。然而，人类大脑具有复杂的解剖结构。同时，脑部磁共振成像（MR）图像在ROI边界周围常常存在低强度对比度、较大的个体间差异和较大的个体内差异。为了解决这些问题，在过去十年中提出了许多基于多图谱的分割方法用于脑ROI分割。本文针对多图谱方法中广泛使用的几种配准工具箱、传统的标签融合方法、用于评估多图谱方法的数据集以及基于多图谱分割在临床研究中的应用，对基于多图谱的脑MR图像分割方法进行了综述。我们提出，将解剖学先验知识纳入用于脑ROI分割的端到端深度学习架构是未来的一个重要方向。
