Image Based Brain Segmentation: From Multi-atlas Fusion to Deep Learning

被引：8
|
作者：
Lin, Xiangbo [1 ]
Li, Xiaoxi [1 ]
机构：
[1] Dalian Univ Technol, Sch Informat & Commun Engn, Fac Elect Informat & Elect Engn, Dalian 116024, Liaoning, Peoples R China
来源：
CURRENT MEDICAL IMAGING | 2019年 / 15卷 / 05期
基金：
美国国家科学基金会;
关键词：
Brain tissue; segmentation; grand challenge; multi-atlas label fusion; deep learning; algorithms; TISSUE SEGMENTATION; AUTOMATIC SEGMENTATION; NEURAL-NETWORKS; MR-IMAGES; CNN;
D O I：
10.2174/1573405614666180817125454
中图分类号：
R8 [特种医学]; R445 [影像诊断学];
学科分类号：
1002 ; 100207 ; 1009 ;
摘要：
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 winner's 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.
引用
收藏
页码：443 / 452
页数：10
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