Multi-spatial-attention U-Net: a novel framework for automated gallbladder segmentation on CT images
The MSAU-Net V1 and V2 both exhibited outstanding performance in gallbladder segmentation, demonstrating strong potential for clinical application. The MSA block enhances spatial information capture, improving the model's ability to segment small and complex structures with greater precision. These advantages position the MSAU-Net V1 and V2 as valuable tools for broader clinical adoption.
Authors
- Q. Wang (ORCID: https://orcid.org/0000-0001-6245-9974)
- Xiaomeng Ji (ORCID: https://orcid.org/0000-0002-7173-5464)
- Peng Zhan (ORCID: https://orcid.org/0000-0002-3996-7011)
- Henan Lou
- Xiaoyu Sun (ORCID: https://orcid.org/0000-0001-5337-5680)
- Junlin Xu (ORCID: https://orcid.org/0000-0003-1057-1504)
- Fu-Huei Lin (ORCID: https://orcid.org/0000-0002-4679-7348)
- Huiyu Wang (ORCID: https://orcid.org/0000-0002-0181-5178)
- Yi Dong (ORCID: https://orcid.org/0000-0002-0212-1477)
- Jiulun Fan (ORCID: https://orcid.org/0000-0002-7553-204X)
- Ruimei Ren
- Xiaobo Wen (ORCID: https://orcid.org/0000-0002-2433-3702)
Institutions
- Qingdao University (CN)
- People’s Hospital of Rizhao (CN)
- Affiliated Hospital of Qingdao University (CN)
- Qingdao Eighth People's Hospital (CN)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2025-05-30
- DOI
- https://doi.org/10.1186/s12880-025-01737-7
- Primary Topic
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Natural Science Foundation of Shandong Province
- Chinese Society of Clinical Oncology