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

[ Vol. 15 , Issue. 5 ]

Author(s):

Xiangbo Lin* and Xiaoxi Li   Pages 443 - 452 ( 10 )

Abstract:


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.

Keywords:

Brain tissue, segmentation, grand challenge, multi-atlas label fusion, deep learning, algorithms.

Affiliation:

Faculty of Electronic Information and Electrical Engineering, School of Information and Communication Engineering, Dalian University of Technology, Dalian, LiaoNing Province, Faculty of Electronic Information and Electrical Engineering, School of Information and Communication Engineering, Dalian University of Technology, Dalian, LiaoNing Province

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