Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

To better understand early brain growth patterns in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; h...

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Vydáno v:arXiv.org
Hlavní autoři: Sun, Yue, Gao, Kun, Wu, Zhengwang, Lei, Zhihao, Wei, Ying, Ma, Jun, Yang, Xiaoping, Xue, Feng, Zhao, Li, Trung Le Phan, Shin, Jitae, Zhong, Tao, Zhang, Yu, Yu, Lequan, Li, Caizi, Basnet, Ramesh, Ahmad, M Omair, Swamy, M N S, Ma, Wenao, Dou, Qi, Bui, Toan Duc, Camilo Bermudez Noguera, Landman, Bennett, Gotlib, Ian H, Humphreys, Kathryn L, Shultz, Sarah, Li, Longchuan, Niu, Sijie, Lin, Weili, Jewells, Valerie, Li, Gang, Shen, Dinggang, Wang, Li
Médium: Paper
Jazyk:angličtina
Vydáno: Ithaca Cornell University Library, arXiv.org 11.07.2020
Témata:
Algorithms
Brain
Cerebrospinal fluid
Datasets
Image segmentation
Machine learning
Magnetic resonance
Performance evaluation
Scanners
ISSN:2331-8422
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Popis
Shrnutí:To better understand early brain growth patterns in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. Training/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participating in iSeg-2019. We review the 8 top-ranked teams by detailing their pipelines/implementations, presenting experimental results and evaluating performance in terms of the whole brain, regions of interest, and gyral landmark curves. We also discuss their limitations and possible future directions for the multi-site issue. We hope that the multi-site dataset in iSeg-2019 and this review article will attract more researchers on the multi-site issue.
Bibliografie:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.2007.02096