LC-YOLOmatch: a novel scene segmentation approach based on YOLO for laparoscopic cholecystectomy
These improvements not only highlight its potential in automated surgeries but also significantly enhance assistance in laparoscopic procedures while effectively reducing the incidence of complications.
Authors
- Shuai Xiao (ORCID: https://orcid.org/0000-0003-4058-8120)
- Han Wang (ORCID: https://orcid.org/0000-0002-5002-3708)
- Yuancheng Shao (ORCID: https://orcid.org/0009-0006-4063-2186)
- Fengshi Jing (ORCID: https://orcid.org/0000-0002-6747-6527)
- Long Hong (ORCID: https://orcid.org/0009-0005-8314-4353)
- Jia Gu (ORCID: https://orcid.org/0000-0002-0642-3966)
- Yuqiao Chen
Institutions
- University of North Carolina at Chapel Hill (US)
- Jinan University (CN)
- University of Macau (MO)
- Zhuhai People's Hospital (CN)
- Suzhou Vocational Health College (CN)
- City University of Macau (MO)
- University of South China (CN)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-12-08
- DOI
- https://doi.org/10.3389/frai.2025.1706021
- Primary Topic
- Minimally Invasive Surgical Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00