PRS-Net: A Few-shot Network for Perirenal Fat and Renal Parenchyma Semantic Segmentation
This work proposes a Few-shot network employed for Perirenal Fat and Renal Parenchyma Semantic Segmentation (PRS-Net) in Diabetic Kidney Disease (DKD). PRS-Net integrates ST module and Fusion module. These two modules allow the model to process CT images with different spatial distributions and fuse multi-scale features, thereby enhancing performance in image segmentation. Utilizing the Perirenal Fat and Renal Parenchyma Dataset for semantic segmentation, PRS-Net achieves a mean intersection over union of 60.22% on test set, achieving superior performance compared to the other models. PRS-Net has clinical significance for early DKD diagnosis.
Authors
- Kunyang Teng (ORCID: https://orcid.org/0000-0003-2396-5414)
- Hongzan Sun (ORCID: https://orcid.org/0000-0002-4724-5828)
- Xiangyu Meng (ORCID: https://orcid.org/0000-0002-8886-1408)
- Marcin Grzegorzek (ORCID: https://orcid.org/0000-0003-4877-8287)
- Tianming Du (ORCID: https://orcid.org/0000-0001-7671-6513)
- Tao Jiang (ORCID: https://orcid.org/0000-0003-1908-2926)
- Chen Li (ORCID: https://orcid.org/0000-0003-1545-8885)
- Shuai Ye (ORCID: https://orcid.org/0000-0001-9604-0063)
- Shuaiyi Tian
- Zihao Wang (ORCID: https://orcid.org/0009-0005-1043-2215)
Institutions
- Chengdu University of Traditional Chinese Medicine (CN)
- China Medical University (CN)
- Northeastern University (CN)
Publication Details
- Published
- 2024-12-15
- DOI
- https://doi.org/10.1109/bigdata62323.2024.10825826
- Citations
- 2
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.37
Funders
- National Natural Science Foundation of China