Dual-Scale SE UNet: Uma Rede Neural Convolucional Aprimorada com SE Blocks para Segmentação Renal em Imagens de TC
Resumo
O câncer renal é uma das neoplasias mais comuns do trato geniturinário, tornando a segmentação automática uma ferramenta essencial para auxiliar no diagnóstico precoce. Este trabalho visa desenvolver e avaliar uma rede neural para segmentação de imagens de tomografia computadorizada, identificando rins, tumores e cisto. Propomos a substituição das camadas convolucionais da U-Net por blocos Dual-Scale SE para aprimorar a extração de características. A metodologia proposta obteve um coeficiente Dice de 0,93 e 0,86, respectivamente.
Referências
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.
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.
Publicado
09/06/2025
Como Citar
OLIVEIRA, Marcus Vinicius; MATOS, Caio Eduardo Falcão; BRAZ JÚNIOR, Geraldo; ALMEIDA, João Dallyson Sousa de.
Dual-Scale SE UNet: Uma Rede Neural Convolucional Aprimorada com SE Blocks para Segmentação Renal em Imagens de TC. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 760-771.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2025.7747.
