摘要
Brain region-of-interesting (ROI) segmentation is an important prerequisite step for many computeraid 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 multiatlas 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.
Brain region-of-interesting(ROI) segmentation is an important prerequisite step for many computeraid 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 multiatlas 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.
基金
Supported by the National Natural Science Foundation of China(Nos.61876082,61861130366,61703301)
the Jiangsu Provincial 333 High-level Talent Cultivation Projects~~
