Metastasis lesion segmentation from bone scintigrams using encoder-decoder architecture model with multi-attention and multi-scale learning
The proposed segmentation model can be used as a promising tool for automatically extracting metastasis lesions from SPECT bone scintigrams, offering significant support to the development of deep learning-based automated analysis for characterizing BM.
Authors
- Yang He (ORCID: https://orcid.org/0000-0002-4952-6877)
- Zhengxing Man
- Xianwu Zeng
- Ailing Xie (ORCID: https://orcid.org/0000-0001-7725-6897)
- Qiang Lin (ORCID: https://orcid.org/0000-0002-3842-2634)
- Yongchun Cao (ORCID: https://orcid.org/0009-0003-1729-288X)
- Caihong Liu (ORCID: https://orcid.org/0009-0008-5287-4064)
- Xiaodi Huang (ORCID: https://orcid.org/0000-0002-6788-9305)
- Yusheng Hao
Institutions
- Minzu University of China (CN)
- Charles Sturt University (AU)
- Gansu Provincial Hospital (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2024-12-31
- DOI
- https://doi.org/10.21037/qims-24-1246
- Citations
- 3
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Gansu Province
- National Ethnic Affairs Commission of the People's Republic of China
- Fundamental Research Funds for the Central Universities