Xiangbo Lin 1 , Xiaoxi Li 1 Show Affiliations »
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. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
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. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
Keywords:
Brain tissue; algorithms; deep learning; grand challenge; multi-atlas label fusion; segmentation
Mesh： See more »
Year: 2019
PMID： 32008551 DOI： 10.2174/1573405614666180817125454
Source DB: PubMed Journal: Curr Med Imaging Rev ISSN： 1573-4056