Kidney Tumor Image Segmentation Algorithm based on Deep Learning
DOI:
https://doi.org/10.54097/2h3wmj52Keywords:
Renal Tumor, Image Segmentation, Deep Learning, Segmentation Model, Feature FusionAbstract
This paper focuses on the segmentation algorithm of kidney tumor image based on deep learning, expounds the research significance of the subject, combs the research status at home and abroad in this field, and defines the research methods and technical routes. In this paper, the overall design of the algorithm is completed, and the preprocessing work such as data collection and annotation of kidney tumor image is completed. On the basis of v-net neural network, the feature fusion module and dual attention mechanism are introduced to improve the segmentation model. By setting experimental parameters to carry out experiments, the segmentation effect of the model is analyzed from the qualitative and quantitative perspectives. This study can provide technical support for the clinical diagnosis of renal tumor, and provide reference for algorithm optimization in related fields.
Downloads
References
[1] Ye, R. G., Chen, Y. S., & Fang, J. A. (2003). Summary of the Symposium on the diagnosis, treatment and efficacy criteria of kidney disease. Chinese Journal of Integrated Traditional and Western Medicine Nephrology, 4(5), 249–251.
[2] Lu, C., Yang, S. F., & Yue, H. (2008). Epidemiological investigation status of chronic kidney disease. Medical Review, 14(3), 370–372.
[3] Agnes, A. S., Anitha, J., & Peter, D. J. (2018). Automatic lung segmentation in low-dose chest CT scans using convolutional deep and wide network (CDWN). Neural Computing and Applications, 32(20), 1–11. https://doi.org/10.1007/s00521-018-3745-6.
[4] Sun, Q., Dai, M., Lan, Z., et al. (2022). UCR-Net: U-shaped context residual network for medical image segmentation. Computers in Biology and Medicine, 151, 106203. https:// doi. org/ 10.1016/j.compbiomed.2022.106203.
[5] Zhang, N., Yu, L., Zhang, D., et al. (2022). APT-Net: Adaptive encoding and parallel decoding transformer for medical image segmentation. Computers in Biology and Medicine, 151, 106292. https://doi.org/10.1016/j.compbiomed.2022.106292.
[6] Belgherbi, A., Hadjidj, I., & Bessaid, A. (2014). Morphological segmentation of the kidneys from abdominal CT images. Journal of Mechanics in Medicine and Biology, 14(5), 11–12. https://doi.org/10.1142/S0219519414500573.
[7] Yang, G., Gu, J., Chen, Y., et al. (2014). Automatic kidney segmentation in CT images based on multi-atlas image registration. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 5538–5541). IEEE. https://doi.org/10. 1109/EMBC. 2014. 694 4894.
[8] Lin, D. T., Lei, C. C., & Hung, S. W. (2006). Computer-aided kidney segmentation on abdominal CT images. IEEE Transactions on Information Technology in Biomedicine, 10(1), 59–65. https://doi.org/10.1109/TITB.2005.855567.
[9] Sun, P., Mo, Z., Hu, F., et al. (2022). Segmentation of kidney mass using AgDenseU-Net 2.5 D model. Computers in Biology and Medicine, 150, 106223. https://doi.org/10.1016/j. compbiomed. 2022.106223.
[10] Huang, Y., Jiao, J., Yu, J., et al. (2023). RsALUNet: A reinforcement supervision U-Net-based framework for multi-ROI segmentation of medical images. Biomedical Signal Processing and Control, 84, 104743. https://doi.org/10. 1016/j. bspc.2023.104743.
[11] Zhou, Y., Jiang, H., Diao, Z., et al. (2023). MRLA-Net: A tumor segmentation network embedded with a multiple receptive-field lesion attention module in PET-CT images. Computers in Biology and Medicine, 153, 106538. https://doi. org/ 10.1016/j.compbiomed.2023.106538.
[12] Zhang, F., Hao, S. M., & Geng, L. (2023). Segmentation method of kidney and renal tumor based on deep multi-scale aggregation 3D U-Net. Journal of Tianjin University of Technology, 42(6), 84–90.
[13] Alexandru, B. M., & Popescu, D. (2025). Two-Stage Neural Network Pipeline for Kidney and Tumor Segmentation. IEEE Access, 1–12. https://doi.org/10.1109/ACCESS.2025.3492761.
[14] Milletari, F., Navab, N., & Ahmadi, S. A. (2016). V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 Fourth International Conference on 3D Vision (3DV) (pp. 565–571). IEEE. https://doi.org/10. 1109/ 3DV.2016.68.
[15] Pabitha, C., Benila, S., & Vanathi, B. (2025). An adaptive recalibrative contextual squeeze-and-excitation self-attention V-Net for kidney tumor segmentation in RCC imaging. The European Physical Journal Plus, 140(8), 1–22. https://doi.org/ 10.1140/epjp/s13360-025-05879-9.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Frontiers in Computing and Intelligent Systems
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
