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DeepWMH: A deep learning tool for accurate white matter hyperintensity segmentation without requiring manual annotations for training.

作者信息

Liu Chenghao, Zhuo Zhizheng, Qu Liying, Jin Ying, Hua Tiantian, Xu Jun, Tan Guirong, Li Yuna, Duan Yunyun, Wang Tingting, Zhang Zaiqiang, Zhang Yanling, Chen Rui, Yu Pinnan, Zhang Peixin, Shi Yulu, Zhang Jianguo, Tian Decai, Li Runzhi, Zhang Xinghu, Shi Fudong, Wang Yanli, Jiang Jiwei, Carass Aaron, Liu Yaou, Ye Chuyang

机构信息

School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China.

Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.

出版信息

Sci Bull (Beijing). 2024 Apr 15;69(7):872-875. doi: 10.1016/j.scib.2024.01.034. Epub 2024 Jan 28.

DOI:10.1016/j.scib.2024.01.034
PMID:38320896
Abstract
摘要

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Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation.
多阶段半监督学习增强了脑白质高信号分割。
Front Comput Neurosci. 2024 Oct 22;18:1487877. doi: 10.3389/fncom.2024.1487877. eCollection 2024.