Developing a segmentation cascade deep learning network based on automated prompts
This work presents a computationally efficient, two-stage deep learning-based auto-segmentation (DLAS) framework that significantly improves the accuracy and reliability of GTVnx segmentation. It improves the accuracy and reliability of GTVnx segmentation, features a lightweight architecture and high scalability, and highlights a promising pathway for clinical integration.
Authors
- Xuena Yan
- Kuo Men (ORCID: https://orcid.org/0000-0001-9754-2241)
- Shiran Sun
- Yuhe Yao
- Junlin Yi
- Xinyuan Chen
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2026-02-13
- DOI
- https://doi.org/10.21037/qims-2025-1163
- Primary Topic
- Anomaly Detection Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Chinese Academy of Medical Sciences