Dual-Scale SE UNet: A SE Block-Enhanced Convolutional Neural Network for Kidney Segmentation in CT Images

• Marcus Vinicius Oliveira UFMA
• Caio Eduardo Falcão Matos UFMA
• Geraldo Braz Júnior UFMA
• João Dallyson Sousa de Almeida UFMA

DOI: https://doi.org/10.5753/sbcas.2025.7747

Abstract

Renal cancer is one of the most common neoplasms of the genitourinary tract, making automatic segmentation an essential tool for assisting in early diagnosis. This work aims to develop and evaluate a neural network for segmenting computed tomography images and identifying kidneys, tumors and cyst. We propose replacing the standard U-Net convolutional layers with Dual-Scale SE blocks to enhance feature extraction. The proposed methodology achieved a Dice coefficient of 0.93 and 0.86, respectively.

References

American Cancer Society (2024). What is kidney cancer? Disponível em: [link]. Acessado em: 1 de maio de 2024.

Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B. (2011). Algorithms for hyperparameter optimization. Advances in neural information processing systems, 24.

Brenner, D. J. and Hall, E. J. (2007). Computed tomography—an increasing source of radiation exposure. New England journal of medicine, 357(22):2277–2284.

Fleiss, J. L., Levin, B., Paik, M. C., et al. (1981). The measurement of interrater agreement. Statistical methods for rates and proportions, 2(212-236):22–23.

George, Y. (2022). A coarse-to-fine 3d u-net network for semantic segmentation of kidney ct scans. In Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Papanikolopoulos, N., and Weight, C., editors, Kidney and Kidney Tumor Segmentation, pages 137–142, Cham. Springer International Publishing.

Gloeckler Ries, L. A., Reichman, M. E., Lewis, D. R., Hankey, B. F., and Edwards, B. K. (2003). Cancer survival and incidence from the surveillance, epidemiology, and end results (seer) program. The Oncologist, 8(6):541–552.

Golts, A., Khapun, D., Shats, D., Shoshan, Y., and Gilboa-Solomon, F. (2022). An ensemble of 3d u-net based models for segmentation of kidney and masses in ct scans. In Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Papanikolopoulos, N., and Weight, C., editors, Kidney and Kidney Tumor Segmentation, pages 103–115, Cham. Springer International Publishing.

Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Zhao, Z., Chen, H., Wang, L., Golts, A., Khapun, D., Shats, D., Shoshan, Y., Gilboa-Solomon, F., George, Y., Yang, X., Zhang, J., Zhang, J., Xia, Y., Wu, M., Liu, Z., Walczak, E., McSweeney, S., Vasdev, R., Hornung, C., Solaiman, R., Schoephoerster, J., Abernathy, B., Wu, D., Abdulkadir, S., Byun, B., Spriggs, J., Struyk, G., Austin, A., Simpson, B., Hagstrom, M., Virnig, S., French, J., Venkatesh, N., Chan, S., Moore, K., Jacobsen, A., Austin, S., Austin, M., Regmi, S., Papanikolopoulos, N., and Weight, C. (2023). The kits21 challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase ct.

Heller, N., Sathianathen, N., Kalapara, A., Walczak, E., Moore, K., Kaluzniak, H., Rosenberg, J., Blake, P., Rengel, Z., Oestreich, M., Dean, J., Tradewell, M., Shah, A., Tejpaul, R., Edgerton, Z., Peterson, M., Raza, S., Regmi, S., Papanikolopoulos, N., and Weight, C. (2019). The kits19 challenge data: 300 kidney tumor cases with clinical context, ct semantic segmentations, and surgical outcomes. Disponível em: [link].

Hou, X., Xie, C., Li, F., and Nan, Y. (2019). Cascaded semantic segmentation for kidney and tumor. Submissions to the.

Hu, J., Shen, L., Albanie, S., Sun, G., and Wu, E. (2017). Squeeze-and-excitation networks. arXiv preprint arXiv:1709.01507, updated in 2019.

Isensee, F. and Maier-Hein, K. H. (2019). An attempt at beating the 3d u-net. arXiv preprint arXiv:1908.02182.

Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2018). Focal loss for dense object detection.

Long, J., Shelhamer, E., and Darrell, T. (2014). Fully convolutional networks for semantic segmentation.

Matos, C., Oliveira, M., Diniz, J., Fernandes, A., Junior, G. B., and Paiva, A. (2023). Ppm-deeplab: Módulo de pirâmide de pooling como codificador da rede deeplabv3+ para segmentação de rins, cistos e tumores renais. In Anais do XXIII Simpósio Brasileiro de Computação Aplicada à Saúde, pages 210–221, Porto Alegre, RS, Brasil. SBC.

Oliveira, M., Matos, C., Júnior, G. B., Paiva, A., Almeida, J., Costa, G., Levy, M., and Freitas, M. (2022). Ppm-unet: Uma rede neural convolucional para a segmentação de rins em imagens de tc. In Anais do XXII Simpósio Brasileiro de Computação Aplicada à Saúde, pages 268–276, Porto Alegre, RS, Brasil. SBC.

Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation.

Siegel, R. L., Giaquinto, A. N., and Jemal, A. (2024). Cancer statistics, 2024. CA: A Cancer Journal for Clinicians, 74(1):12–49.

Stewart, B. W., Wild, C. P., et al. (2014). World cancer report 2014. IARC Press, International Agency for Research on Cancer, Lyon, France.

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Türk, F., Lüy, M., and Barışçı, N. (2020). Kidney and renal tumor segmentation using a hybrid v-net-based model. Mathematics, 8(10):1772.

Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., Liu, W., and Xiao, B. (2019). Deep high-resolution representation learning for visual recognition. TPAMI.

Zhang, Y., Wang, Y., Hou, F., Yang, J., Xiong, G., Tian, J., and Zhong, C. (2019). Cascaded volumetric convolutional network for kidney tumor segmentation from ct volumes. arXiv preprint arXiv:1910.02235.

Zhao, Z., Chen, H., and Wang, L. (2022). A coarse-to-fine framework for the 2021 kidney and kidney tumor segmentation challenge. In Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Papanikolopoulos, N., and Weight, C., editors, Kidney and Kidney Tumor Segmentation, pages 53–58, Cham. Springer International Publishing.