Deep-learning-based automatic liver segmentation using computed tomography images in dogs
The manual and predicted liver volumes showed excellent agreement, highlighting the potential clinical applicability of this approach.
Authors
- Ren Togo (ORCID: https://orcid.org/0000-0002-4474-3995)
- Takahiro Ogawa (ORCID: https://orcid.org/0000-0001-5332-8112)
- Kensuke Nakamura (ORCID: https://orcid.org/0000-0002-1010-3228)
- Nozomu Yokoyama (ORCID: https://orcid.org/0000-0003-2077-0523)
- Mitsuyoshi Takiguchi (ORCID: https://orcid.org/0000-0001-7648-7036)
- Genya Shimbo (ORCID: https://orcid.org/0000-0002-2903-3610)
- Seungyeon Lee (ORCID: https://orcid.org/0000-0002-4196-1049)
Institutions
- Hokkaido University of Science (JP)
- Hokkaido University (JP)
- Hokkaido University Hospital (JP)
Publication Details
- Journal
- Frontiers in Veterinary Science
- Published
- 2025-10-21
- DOI
- https://doi.org/10.3389/fvets.2025.1681820
- Primary Topic
- Medical Image Segmentation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Hokkaido University
- Japan Society for the Promotion of Science