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

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

doi:10.1016/j.scib.2024.01.034

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

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

^*Corresponding author for this work

School of Integrated Circuits and Electronics

Research output: Contribution to journal › Letter › peer-review

1 Citation (Scopus)

Original language	English
Pages (from-to)	872-875
Number of pages	4
Journal	Science Bulletin
Volume	69
Issue number	7
DOIs	https://doi.org/10.1016/j.scib.2024.01.034
Publication status	Published - 15 Apr 2024

Access to Document

10.1016/j.scib.2024.01.034

Cite this

Liu, C., Zhuo, Z., Qu, L., Jin, Y., Hua, T., Xu, J., Tan, G., Li, Y., Duan, Y., Wang, T., Zhang, Z., Zhang, Y., Chen, R., Yu, P., Zhang, P., Shi, Y., Zhang, J., Tian, D., Li, R., ... Ye, C. (2024). DeepWMH: A deep learning tool for accurate white matter hyperintensity segmentation without requiring manual annotations for training. Science Bulletin, 69(7), 872-875. https://doi.org/10.1016/j.scib.2024.01.034

@article{b4600b7a4a5045969fe2b77b52ca24b1,

title = "DeepWMH: A deep learning tool for accurate white matter hyperintensity segmentation without requiring manual annotations for training",

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

year = "2024",

month = apr,

day = "15",

doi = "10.1016/j.scib.2024.01.034",

language = "English",

volume = "69",

pages = "872--875",

journal = "Science Bulletin",

issn = "2095-9273",

publisher = "Elsevier B.V.",

number = "7",

}

Liu, C, Zhuo, Z, Qu, L, Jin, Y, Hua, T, Xu, J, Tan, G, Li, Y, Duan, Y, Wang, T, Zhang, Z, Zhang, Y, Chen, R, Yu, P, Zhang, P, Shi, Y, Zhang, J, Tian, D, Li, R, Zhang, X, Shi, F, Wang, Y, Jiang, J, Carass, A, Liu, Y & Ye, C 2024, 'DeepWMH: A deep learning tool for accurate white matter hyperintensity segmentation without requiring manual annotations for training', Science Bulletin, vol. 69, no. 7, pp. 872-875. https://doi.org/10.1016/j.scib.2024.01.034

TY - JOUR

T1 - DeepWMH

T2 - A deep learning tool for accurate white matter hyperintensity segmentation without requiring manual annotations for training

AU - Liu, Chenghao

AU - Zhuo, Zhizheng

AU - Qu, Liying

AU - Jin, Ying

AU - Hua, Tiantian

AU - Xu, Jun

AU - Tan, Guirong

AU - Li, Yuna

AU - Duan, Yunyun

AU - Wang, Tingting

AU - Zhang, Zaiqiang

AU - Zhang, Yanling

AU - Chen, Rui

AU - Yu, Pinnan

AU - Zhang, Peixin

AU - Shi, Yulu

AU - Zhang, Jianguo

AU - Tian, Decai

AU - Li, Runzhi

AU - Zhang, Xinghu

AU - Shi, Fudong

AU - Wang, Yanli

AU - Jiang, Jiwei

AU - Carass, Aaron

AU - Liu, Yaou

AU - Ye, Chuyang

PY - 2024/4/15

Y1 - 2024/4/15

UR - http://www.scopus.com/inward/record.url?scp=85189489717&partnerID=8YFLogxK

U2 - 10.1016/j.scib.2024.01.034

DO - 10.1016/j.scib.2024.01.034

M3 - Letter

AN - SCOPUS:85189489717

SN - 2095-9273

VL - 69

SP - 872

EP - 875

JO - Science Bulletin

JF - Science Bulletin

IS - 7

ER -

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

Access to Document

Other files and links

Cite this