End‐to‐end deep learning approach for uterine artery‐ovarian artery anastomosis detection from digital subtraction angiography sequences
UOA-D demonstrated superior efficacy in detecting UOA through digital subtraction angiography (DSA) sequences, offering considerable advantages for clinical application.
Authors
- Li Yi (ORCID: https://orcid.org/0000-0003-0216-831X)
- Yanqiao Ren (ORCID: https://orcid.org/0000-0001-8086-4527)
- Xiaoyun Liang (ORCID: https://orcid.org/0000-0002-1851-3408)
- Jinqiang Ma (ORCID: https://orcid.org/0000-0001-6917-6029)
- Tao Sun (ORCID: https://orcid.org/0000-0001-5931-386X)
- Lei Chen (ORCID: https://orcid.org/0000-0002-9582-922X)
- Huangxuan Zhao (ORCID: https://orcid.org/0000-0002-9995-7528)
- Chuansheng Zheng (ORCID: https://orcid.org/0000-0002-2435-1417)
- Dongqiao Xiang
- Yu Lei
Institutions
- Union Hospital (HK)
- State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN)
- Wuhan Union Hospital (CN)
- Neusoft (China) (CN)
- Union Hospital (CN)
- Huazhong University of Science and Technology (CN)
- Northeastern University (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-10-24
- DOI
- https://doi.org/10.1002/mp.70083
- Primary Topic
- Uterine Myomas and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China