AutoPrompt-SAM3D: integrated generation and selection for SAM2-based 3D medical segmentation
AutoPrompt-SAM3D eliminates the dependency on manual prompts in SAM2-based 3D segmentation through hierarchical feature integration and error filtering. By enhancing both the reliability and efficiency of tumor localization, this framework provides a practical tool for large-scale medical image analysis and supports more consistent clinical decision-making.
Authors
- Wanqiu Cheng
- Shun Li (ORCID: https://orcid.org/0000-0003-1102-5572)
- Ting Deng (ORCID: https://orcid.org/0000-0001-5477-7972)
- Jintao Tang
- Ting Wang
Institutions
- National University of Defense Technology (CN)
- Tianjin Medical University (CN)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2026-03-28
- DOI
- https://doi.org/10.1186/s12859-026-06390-7
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00