Prior information guided deep-learning model for tumor bed segmentation in breast cancer radiotherapy
Peng Huang, Hui Yan, Jiawen Shang, Xin Xie · 2024
Read Prior information guided deep-learning model for tumor bed segmentation in breast cancer radiotherapy by Peng Huang, Hui Yan, Jiawen Shang, Xin Xie (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11571877.txt.
Description
Peng Huang, Hui Yan, Jiawen Shang and Xin Xie taught a deep learning model to outline the tumour bed for breast radiotherapy in 2024. After breast-conserving surgery, planners must find the cavity where the tumour sat, but it blends into soft tissue on CT scans.
Their trick is prior knowledge: the tumour outline drawn on the pre-surgery scan is warped onto the post-surgery scan and fed to the model alongside it. With that guidance, the overlap score between model and expert outlines jumped from 0.520 to 0.812, far above a simple grey-level threshold method. The approach could speed up treatment planning and make target drawing more consistent between clinicians.
Catalog record
| CREATOR | Peng Huang, Hui Yan, Jiawen Shang, Xin Xie |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 28 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |