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

To better understand early brain development 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 owe...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 40; H. 5; S. 1363 - 1376
Hauptverfasser:	Sun, Yue, Gao, Kun, Wu, Zhengwang, Li, Guannan, Zong, Xiaopeng, Lei, Zhihao, Wei, Ying, Ma, Jun, Yang, Xiaoping, Feng, Xue, Zhao, Li, Le Phan, Trung, 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, Noguera, Camilo Bermudez, Landman, Bennett, Gotlib, Ian H., Humphreys, Kathryn L., Shultz, Sarah, Li, Longchuan, Niu, Sijie, Lin, Weili, Jewells, Valerie, Shen, Dinggang, Li, Gang, Wang, Li
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.05.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Brain Brain - diagnostic imaging Brain Mapping Cerebrospinal fluid Datasets Deep learning domain adaptation Gray Matter Humans Image segmentation Infant Infant brain segmentation isointense phase low tissue contrast Machine learning Magnetic resonance Magnetic Resonance Imaging Manuals multi-site issue Neuroimaging Pediatrics Performance evaluation Scanners Substantia alba Substantia grisea Testing Training
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
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