A generative whole-brain segmentation model for positron emission tomography images
For automatic and accurate whole-brain segmentation, we propose a novel 3D generative multi-object segmentation model for brain PET images, which achieves superior model performance compared with other deep learning methods. In the future, we will apply our whole-brain segmentation method to clinical practice and extend it to other multimodal tasks.
Authors
- Yunlong Gao (ORCID: https://orcid.org/0009-0009-4843-6376)
- Yaping Wu (ORCID: https://orcid.org/0000-0002-2979-5332)
- Zhenxing Huang (ORCID: https://orcid.org/0000-0001-5012-1992)
- Zhanli Hu (ORCID: https://orcid.org/0000-0003-0618-6240)
- Jian‐Min Yuan (ORCID: https://orcid.org/0000-0002-4620-3108)
- Wenbo Li (ORCID: https://orcid.org/0000-0002-0199-1534)
- Na Zhang (ORCID: https://orcid.org/0000-0002-9888-7396)
- Yang Yang (ORCID: https://orcid.org/0000-0002-1364-8653)
- Yan Zhang (ORCID: https://orcid.org/0000-0002-4055-4863)
- Jing Qin (ORCID: https://orcid.org/0000-0002-7059-0929)
- Meiyun Wang (ORCID: https://orcid.org/0000-0001-8779-6184)
- Hongyan Tang
- Dong Liang (ORCID: https://orcid.org/0000-0003-1965-5368)
- Hairong Zheng
Institutions
- Hong Kong Polytechnic University (HK)
- Chinese Academy of Sciences (CN)
- Zhengzhou University (CN)
- Academy of Mathematics and Systems Science (CN)
- Henan Provincial People's Hospital (CN)
- United Imaging Healthcare (China) (CN)
- Shenzhen Institutes of Advanced Technology (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- EJNMMI Physics
- Published
- 2025-02-08
- DOI
- https://doi.org/10.1186/s40658-025-00716-9
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangdong Province
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province
- National Key Research and Development Program of China
- Shenzhen Science and Technology Innovation Program