Automatic dual‐modality breast tumor segmentation in PET/CT images using CT‐guided transformer
We present a deep-learning-based method for the joint segmentation of anatomical and functional PET/CT images. Compared to single-modality and dual-modality methods with various fusion strategies, our approach significantly improves the accuracy of breast tumor delineation, demonstrating great potential for breast tumor diagnosis.
Authors
- Xiangjian He (ORCID: https://orcid.org/0000-0001-8962-540X)
- Huizhong Zheng (ORCID: https://orcid.org/0000-0003-1658-6467)
- Zhenxing Huang (ORCID: https://orcid.org/0000-0001-5012-1992)
- Zhanli Hu (ORCID: https://orcid.org/0000-0003-0618-6240)
- Yuan Yao (ORCID: https://orcid.org/0000-0002-2705-6245)
- Dan Shao (ORCID: https://orcid.org/0000-0002-9925-0936)
- Yongfeng Yang
- Dong Liang (ORCID: https://orcid.org/0000-0001-6257-0875)
- Hairong Zheng
Institutions
- University of Nottingham Ningbo China (CN)
- Academy of Mathematics and Systems Science (CN)
- Shenzhen Institutes of Advanced Technology (CN)
- Guangdong Provincial People's Hospital (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-11-01
- DOI
- https://doi.org/10.1002/mp.70136
- Citations
- 1
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangdong Province
- National Key Research and Development Program of China