Development and evaluation of a deep learning framework for pelvic and sacral tumor segmentation from multi-sequence MRI: a retrospective study
In this study, we developed a practical DL framework for PST segmentation via multi-sequence MRI, which reduces the dependence on data annotation. These models offer solutions for various clinical scenarios and have significant potential for wide-ranging applications.
Authors
- Weidao Chen (ORCID: https://orcid.org/0000-0001-7730-916X)
- Ping Yin (ORCID: https://orcid.org/0000-0003-0869-3772)
- Qianrui Fan (ORCID: https://orcid.org/0000-0003-3446-7242)
- Nan Hong (ORCID: https://orcid.org/0000-0003-1453-273X)
- Ruize Yu
- Tao Liu (ORCID: https://orcid.org/0000-0001-5347-5892)
- Xia Liu (ORCID: https://orcid.org/0000-0001-7599-6285)
- Dawei Wang (ORCID: https://orcid.org/0000-0001-9981-3623)
Institutions
- Peking University (CN)
- InferVision (China) (CN)
- Peking University People's Hospital (CN)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2025-03-13
- DOI
- https://doi.org/10.1186/s40644-025-00850-8
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Natural Science Foundation of China