MSA-Net: a multi-scale and adversarial learning network for segmenting bone metastases in low-resolution SPECT imaging
Our results demonstrate that the integration of multi-scale feature learning with adversarial supervision significantly improves the segmentation of bone metastasis in SPECT imaging. This approach shows potential for clinical decision support in the management of lung cancer.
Authors
- Jinshui Ji
- Zhengxing Man
- Xianwu Zeng
- Zhengqi Cai
- Xiaodi Huang (ORCID: https://orcid.org/0000-0002-6084-1851)
- Qiang Lin (ORCID: https://orcid.org/0000-0002-3842-2634)
- Yongchun Cao (ORCID: https://orcid.org/0009-0003-1729-288X)
- Yang He (ORCID: https://orcid.org/0009-0008-9009-937X)
- Caihong Liu (ORCID: https://orcid.org/0009-0008-5287-4064)
- Yusheng Wu (ORCID: https://orcid.org/0000-0002-6424-0324)
- Yusheng Hao
Institutions
- Minzu University of China (CN)
- Charles Sturt University (AU)
- Gansu Provincial Hospital (CN)
Publication Details
- Journal
- EJNMMI Physics
- Published
- 2025-07-24
- DOI
- https://doi.org/10.1186/s40658-025-00785-w
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Gansu Province
- Gansu Education Department
- National Ethnic Affairs Commission of the People's Republic of China
- Fundamental Research Funds for the Central Universities