Development and validation of a deep learning-based automatic segmentation and classification of cerebral white matter hyperintensities.

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Title: Development and validation of a deep learning-based automatic segmentation and classification of cerebral white matter hyperintensities.
Authors: Jeong SY; Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea., Jung W; R&D Center, VUNO, Seoul, Republic of Korea.; UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, US., Suh CH; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. chonghyunsuh@amc.seoul.kr., Kim SY; University of Ulsan College of Medicine, Seoul, Republic of Korea., Kim J; R&D Center, VUNO, Seoul, Republic of Korea., Heo H; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea., Shim WH; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea., Lim JS; Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea., Lee JH; Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea., Kim HS; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea., Kim SJ; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Source: European radiology [Eur Radiol] 2026 Jun; Vol. 36 (6), pp. 4812-4823. Date of Electronic Publication: 2025 Dec 15.
Publication Type: Journal Article; Validation Study
Journal Info: Publisher: Springer International Country of Publication: Germany NLM ID: 9114774 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1084 (Electronic) Linking ISSN: 09387994 NLM ISO Abbreviation: Eur Radiol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1432-1084
DOI:10.1007/s00330-025-12174-z