Deep learning-based fine-grained assessment of aneurysm wall characteristics using 4D-CT angiography
This study demonstrates the efficacy of a fine-grained deep learning approach in predicting aneurysm wall characteristics using 4D-CTA. Notably, incorporating an attention-based network structure proved to be particularly effective, contributing to enhanced performance.
Authors
- Yixuan Chen (ORCID: https://orcid.org/0009-0004-3695-8752)
- Teerawat Kumrai (ORCID: https://orcid.org/0000-0002-0094-9545)
- Takuya Maekawa (ORCID: https://orcid.org/0000-0002-7227-580X)
- Shigeo Yamashiro (ORCID: https://orcid.org/0000-0003-4634-803X)
- Haruhiko Kishima (ORCID: https://orcid.org/0000-0002-9041-2337)
- Fujimaro Ishida
- Masatoshi Takagaki (ORCID: https://orcid.org/0000-0001-7100-8366)
- Tsutomu Ichinose (ORCID: https://orcid.org/0000-0002-8576-5074)
- Katsumi Takizawa
- Y. Sugiyama
Institutions
- Saiseikai Kumamoto Hospital (JP)
- Japanese Red Cross Asahikawa Hospital (JP)
- Mie Chuo Medical Center (JP)
- National Hospital Organization (JP)
- The University of Osaka (JP)
Publication Details
- Journal
- PeerJ
- Published
- 2025-05-09
- DOI
- https://doi.org/10.7717/peerj.19393
- Primary Topic
- Intracranial Aneurysms: Treatment and Complications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Core Research for Evolutional Science and Technology