Enhanced Cross-stage-attention U-Net for esophageal target volume segmentation
The reconstruction of the skip concatenation in UNet showed an improvement of performance for esophageal segmentation. The results showed the proposed network had better effect on esophageal GTV and CTV segmentation.
Authors
- Jian Yang (ORCID: https://orcid.org/0000-0003-2059-1912)
- Xiao Lou (ORCID: https://orcid.org/0000-0001-7939-8290)
- Youzhe Zhu (ORCID: https://orcid.org/0000-0002-0374-6890)
- Huazhong Shu (ORCID: https://orcid.org/0000-0002-3833-7915)
- Baosheng Li (ORCID: https://orcid.org/0000-0002-5390-7269)
- Juan Zhu (ORCID: https://orcid.org/0009-0008-3318-1393)
Institutions
- Zhangqiu City People's Hospital (CN)
- Shandong Tumor Hospital (CN)
- Ministry of Education (KR)
- Lishui City People's Hospital (CN)
- Shandong First Medical University (CN)
- Southeast University (CN)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12880-024-01515-x
- Citations
- 3
- Primary Topic
- Medical Image Segmentation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.75
Funders
- Shandong First Medical University