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

So Yeong Jeong¹, Wooseok Jung^2,3, Chong Hyun Suh⁴, Sang Yeong Kim⁵, Jinyoung Kim², Hwon Heo⁶, Woo Hyun Shim⁶, Jae-Sung Lim⁷, Jae-Hong Lee⁷, Ho Sung Kim⁶, Sang Joon Kim⁶

¹Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.

²R&D Center, VUNO, Seoul, Republic of Korea.

³UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, US.

⁴Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. chonghyunsuh@amc.seoul.kr.

⁵University of Ulsan College of Medicine, Seoul, Republic of Korea.

⁶Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

⁷Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

PubMed DOI

Abstract

OBJECTIVES: White matter hyperintensities (WMH) represent neuroimaging markers of cerebral small vessel disease. We aimed to develop and validate a deep learning-based simultaneous, automatic WMH segmentation and classification model in patients with cognitive impairment. MATERIALS AND METHODS: This retrospective study included images from consecutive patients with cognitive impairment from a ter…

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Mentioned Companies

V
VUNO
affiliation

Other Funding

National Research Foundation of Korea(NRF- 2021R1C1C1014413)

MeSH Terms

Humans0Deep Learning*Female0White Matter*Male0Retrospective Studies0Magnetic Resonance Imaging*Aged0Cognitive Dysfunction*Image Interpretation, Computer-Assisted*Neuroimaging*Middle Aged0Aged, 80 and over0Cerebral Small Vessel Diseases*Reproducibility of Results0

Keywords

Automated quantificationClassificationMagnetic resonance imagingSegmentationWhite matter

Article Info

PMID: 41398110
DOI: 10.1007/s00330-025-12174-z
Received: May 13, 2025
Accepted: November 3, 2025

How to Cite

Jeong SY et al. Development and validation of a deep learning-based automatic segmentation and classification of cerebral white matter hyperintensities. Eur Radiol2026;36(6):4812-4823. doi:10.1007/s00330-025-12174-z

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