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

Xiangbo Lin; Xiaoxi Li

doi:10.2174/1573405614666180817125454

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

Curr Med Imaging Rev. 2019;15(5):443-452. doi: 10.2174/1573405614666180817125454.

Authors

Xiangbo Lin¹, Xiaoxi Li¹

Affiliation

¹ Faculty of Electronic Information and Electrical Engineering, School of Information and Communication Engineering, Dalian University of Technology, Dalian, LiaoNing Province, China.

PMID: 32008551
DOI: 10.2174/1573405614666180817125454

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; algorithms; deep learning; grand challenge; multi-atlas label fusion; segmentation.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Algorithms
Brain / diagnostic imaging*
Brain Mapping / methods
Deep Learning*
Humans
Magnetic Resonance Imaging*
Neuroimaging / methods*