Regional CSF volume quantification using deep learning for comparative analysis of brain atrophy in frontotemporal dementia subtypes
This deep learning approach provides a simple, interpretable measure of brain atrophy that differentiates FTD subtypes, requiring only standard MRI with minimal preprocessing, and offers clinical utility.
Authors
- Jun Pyo Kim
- Seongmi Kim (ORCID: https://orcid.org/0009-0004-3144-9908)
- Kian Yew Lim
- Kichang Kwak (ORCID: https://orcid.org/0000-0003-4542-0001)
- Soyeon Yoon
- Seongbeom Park (ORCID: https://orcid.org/0000-0002-0759-6826)
- Kyoungmin Kim (ORCID: https://orcid.org/0009-0000-4161-3573)
- Soo Hyun Cho (ORCID: https://orcid.org/0000-0002-4262-1468)
- Duk L. Na (ORCID: https://orcid.org/0000-0003-1572-7862)
- Jin Seok Ahn
- Hee Jin Kim (ORCID: https://orcid.org/0009-0007-9910-3454)
Institutions
- Samsung Medical Center (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Frontiers in Aging Neuroscience
- Published
- 2025-09-22
- DOI
- https://doi.org/10.3389/fnagi.2025.1631640
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation
- Korea Health Industry Development Institute
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea
- Samsung
- Korea National Institute of Health
- Institute for Information and Communications Technology Promotion