Aprendizado Profundo para Detecção de Cálculos Renais em Imagens de Tomografia Computadorizada
Resumo
A calcificação renal é uma doença comum, geralmente detectada pelos médicos especialistas em exames de tomografia computadorizada (TC). Contudo, a análise desse tipo de exames ainda é uma tarefa desafiadora, o que torna um trabalho repetitivo, fadigante e sujeito a erros. Neste contexto propomos um método para detecção de calcificações, na região renal, em imagens de TC, que pode facilitar a tomada de decisão do especialista. Para o desenvolvimento desse método foi utilizada uma base de imagens privada com 887 amostras provenientes de 20 exames de diferentes pacientes. As imagens foram segmentadas utilizando a Mask-RCNN e para etapa de detecção das calcificação, avaliamos quatro modelos YOLOv5 com diferentes tamanhos da imagem de entrada. O modelo que se destacou foi a YOLOv5n com tamanho de entrada 640×640, obtendo um mAP_50 de 0,89, precisão de 0,87 e recall de 0,85.
Referências
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Pecoits-Filho, R. (2004). Diagnóstico de doença renal crônica: avaliação da função renal. J Bras Nefrol, 26(3):4–5.
Prasetyo, E., Suciati, N., and Fatichah, C. (2020). A comparison of yolo and mask r-cnn for segmenting head and tail of fish. In 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), pages 1–6. IEEE.
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Soares, E. F. and Aragão, G. S. (2013). Algoritmo de apoio para tratamento da litíase renal. In Anais do Congresso de Matemática aplicada à computacional, pages 303–305.
Sozzi, M., Cantalamessa, S., Cogato, A., Kayad, A., and Marinello, F. (2022). Automatic bunch detection in white grape varieties using yolov3, yolov4, and yolov5 deep learning algorithms. Agronomy, 12(2):319.
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Viswanath, K. and Gunasundari, R. (2014). Design and analysis performance of kidney stone detection from ultrasound image by level set segmentation and ann classification. In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pages 407–414. IEEE.
Xu, B., Wang, W., Falzon, G., Kwan, P., Guo, L., Chen, G., Tait, A., and Schneider, D. (2020). Automated cattle counting using mask r-cnn in quadcopter vision system. Computers and Electronics in Agriculture, 171:105300.
Yan, B., Fan, P., Lei, X., Liu, Z., and Yang, F. (2021). A real-time apple targets detection method for picking robot based on improved yolov5. Remote Sensing, 13(9):1619.
Yildirim, K., Bozdag, P. G., Talo, M., Yildirim, O., Karabatak, M., and Acharya, U. R. (2021). Deep learning model for automated kidney stone detection using coronal ct images. Computers in Biology and Medicine, page 104569.
Zhao, T., Yang, Y., Niu, H., Wang, D., and Chen, Y. (2018). Comparing u-net convolutional network with mask r-cnn in the performances of pomegranate tree canopy segmentation. In Multispectral, hyperspectral, and ultraspectral remote sensing technology, techniques and applications VII, volume 10780, pages 210–218. SPIE.
Bayram, A. F., Gurkan, C., Budak, A., and KARATAŞ, H. (2022). A detection and prediction model based on deep learning assisted by explainable artificial intelligence for kidney diseases. Avrupa Bilim ve Teknoloji Dergisi, (40):67–74.
Cui, Y., Sun, Z., Ma, S., Liu, W., Wang, X., Zhang, X., and Wang, X. (2021). Automatic detection and scoring of kidney stones on noncontrast ct images using stone nephrolithometry: Combined deep learning and thresholding methods. Molecular Imaging and Biology, 23(3):436–445.
Davradou, A. (2021). Detection and segmentation of pancreas using morphological snakes and deep convolutional neural networks.
Ebrahimi, S. and Mariano, V. Y. (2015). Image quality improvement in kidney stone detection on computed tomography images. Journal of image and graphics, 3(1):40.
Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 580–587.
He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961–2969.
Masood, A. et al. (2018). Computer-assisted decision support system in pulmonary cancer detection and stage classification on ct images. Journal of Biomedical Informatics, 79:117 – 128.
Memarsadeghi, M., Heinz-Peer, G., Helbich, T. H., Schaefer-Prokop, C., Kramer, G., Scharitzer, M., and Prokop, M. (2005). Unenhanced multi–detector row ct in patients suspected of having urinary stone disease: effect of section width on diagnosis. Radiology, 235(2):530–536.
Montalbo, F. J. P. (2020). A computer-aided diagnosis of brain tumors using a fine-tuned yolo-based model with transfer learning. KSII Transactions on Internet and Information Systems (TIIS), 14(12):4816–4834.
Nithya, A., Appathurai, A., Venkatadri, N., Ramji, D., and Palagan, C. A. (2020). Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images. Measurement, 149:106952.
Padilla, R., Netto, S. L., and Da Silva, E. A. (2020). A survey on performance metrics for object-detection algorithms. In 2020 international conference on systems, signals and image processing (IWSSIP), pages 237–242. IEEE.
Parvathi, S. S. and Jonnadula, H. (2022). An efficient and optimal deep learning architecture using custom u-net and mask r-cnn models for kidney tumor semantic segmentation. International Journal of Advanced Computer Science and Applications.
Pecoits-Filho, R. (2004). Diagnóstico de doença renal crônica: avaliação da função renal. J Bras Nefrol, 26(3):4–5.
Prasetyo, E., Suciati, N., and Fatichah, C. (2020). A comparison of yolo and mask r-cnn for segmenting head and tail of fish. In 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), pages 1–6. IEEE.
Rangel, É. B., Gomes, S. A., and Heilberg, I. P. (2005). Litíase renal no pré e pós-transplante renal. Brazilian Journal of Transplantation, 8(4):440–445.
Soares, E. F. and Aragão, G. S. (2013). Algoritmo de apoio para tratamento da litíase renal. In Anais do Congresso de Matemática aplicada à computacional, pages 303–305.
Sozzi, M., Cantalamessa, S., Cogato, A., Kayad, A., and Marinello, F. (2022). Automatic bunch detection in white grape varieties using yolov3, yolov4, and yolov5 deep learning algorithms. Agronomy, 12(2):319.
Thein, N., Hamamoto, K., Nugroho, H. A., and Adji, T. B. (2018). A comparison of three preprocessing techniques for kidney stone segmentation in ct scan images. In 2018 11th Biomedical Engineering International Conference (BMEiCON), pages 1–5. IEEE.
Thuan, D. (2021). Evolution of yolo algorithm and yolov5: The state-of-the-art object detention algorithm.
Tiago Ferolla Nunes, Denise M. Brunetta, C. M. L. P. C. B. P. J. S. R.-F. t. . I. r. a. (2010). Biblioteca Escolar Em Revista, 43(3):272–282. DOI: https://doi.org/10.11606/issn.2176-7262.v43i3p272-282.
Viswanath, K. and Gunasundari, R. (2014). Design and analysis performance of kidney stone detection from ultrasound image by level set segmentation and ann classification. In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pages 407–414. IEEE.
Xu, B., Wang, W., Falzon, G., Kwan, P., Guo, L., Chen, G., Tait, A., and Schneider, D. (2020). Automated cattle counting using mask r-cnn in quadcopter vision system. Computers and Electronics in Agriculture, 171:105300.
Yan, B., Fan, P., Lei, X., Liu, Z., and Yang, F. (2021). A real-time apple targets detection method for picking robot based on improved yolov5. Remote Sensing, 13(9):1619.
Yildirim, K., Bozdag, P. G., Talo, M., Yildirim, O., Karabatak, M., and Acharya, U. R. (2021). Deep learning model for automated kidney stone detection using coronal ct images. Computers in Biology and Medicine, page 104569.
Zhao, T., Yang, Y., Niu, H., Wang, D., and Chen, Y. (2018). Comparing u-net convolutional network with mask r-cnn in the performances of pomegranate tree canopy segmentation. In Multispectral, hyperspectral, and ultraspectral remote sensing technology, techniques and applications VII, volume 10780, pages 210–218. SPIE.
Publicado
27/06/2023
Como Citar
LIMA, Thiago; CATIELY, Camila; SOUSA, Vitoria; VERAS, Rodrigo; ARAÚJO, Flávio.
Aprendizado Profundo para Detecção de Cálculos Renais em Imagens de Tomografia Computadorizada. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 47-58.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2023.229430.
