基于图像的脑部分割：从多图谱融合到深度学习
Image Based Brain Segmentation: From Multi-Atlas Fusion to Deep Learning.
作者信息
Lin Xiangbo, Li Xiaoxi
机构信息
Faculty of Electronic Information and Electrical Engineering, School of Information and Communication Engineering, Dalian University of Technology, Dalian, LiaoNing Province, China.
出版信息
Curr Med Imaging Rev. 2019;15(5):443-452. doi: 10.2174/1573405614666180817125454.
BACKGROUND
This review aims to identify the development of the algorithms for brain tissue and structure segmentation in MRI images.
DISCUSSION
Starting from the results of the Grand Challenges on brain tissue and structure segmentation held in Medical Image Computing and Computer-Assisted Intervention (MICCAI), this review analyses the development of the algorithms and discusses the tendency from multi-atlas label fusion to deep learning. The intrinsic characteristics of the winners' algorithms on the Grand Challenges from the year 2012 to 2018 are analyzed and the results are compared carefully.
CONCLUSION
Although deep learning has got higher rankings in the challenge, it has not yet met the expectations in terms of accuracy. More effective and specialized work should be done in the future.
背景
本综述旨在确定磁共振成像（MRI）图像中脑组织和结构分割算法的发展情况。
讨论
从医学图像计算与计算机辅助干预（MICCAI）举办的脑组织和结构分割重大挑战的结果出发，本综述分析了算法的发展，并讨论了从多图谱标签融合到深度学习的趋势。分析了2012年至2018年重大挑战中获胜算法的内在特征，并仔细比较了结果。
结论
尽管深度学习在挑战中获得了更高的排名，但在准确性方面尚未达到预期。未来应开展更有效和更具针对性的工作。
Zotero 插件