Geoestatísticas e Deep Features para Diferenciar Glomeruloesclerose Segmentar e Focal de Doença de Lesões Mínimas em Imagens de Biópsia Renal
Resumo
As doenças renais crônicas surgem de patologias agudas ou intermitentes quando não tratadas adequadamente, como a doença de lesões mínimas (DLM) e glomeruloesclerose segmentar e focal (GESF). A identificação precisa dessas duas doenças é de suma importância, pois seus tratamentos e prognósticos são diferentes. Dessa forma, propomos um método capaz de diferenciar a DLM de GESF a partir de imagens de exames patológicos. No método proposto, usamos quatro redes neurais convolucionais pré-treinadas e funções geoestatísticas para extrair características de imagem. Selecionamos 94 características com base em critérios de informação mútua e, na etapa de classificação, usamos um classificador random forest. O método proposto obteve acurácia de 94,3% e índice Kappa de 87,9%, confirmando que nosso método é bastante promissor.Referências
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Chagas, P., Souza, L., Araújo, I., Aldeman, N., Duarte, A., Angelo, M., dos Santos, W. L., and Oliveira, L. (2019). Classification of glomerular hypercellularity using convolutional features and support vector machine. arXiv preprint arXiv:1907.00028.
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. arXiv preprint, pages 1610–02357.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1):37–46.
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3):273– 297.
Cover, T. M. and Thomas, J. A. (1991). Entropy, relative entropy and mutual information. Elements of information theory, 2:1–55.
Ginley, B. G., Tomaszewski, J. E., Jen, K.-Y., Fogo, A., Jain, S., and Sarder, P. (2018). Computational analysis of the structural progression of human glomeruli in diabetic nephropathy. In Proceedings of SPIE Medical Imaging, volume 10581, pages 105810A–1–105810A–6.
Haralick, R. M., Shanmugam, K., et al. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, 3(6):610–621.
He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
Ho, T. K. (1995). Random decision forests. In Proceedings of 3rd International Conference on Document Analysis and Recognition, volume 1, pages 278–282.
Isaaks, E., Srivastava, R., and (Firm), K. (1989). An introduction to Applied Geostatistics. Oxford University Press.
Marsh, J. N., Matlock, M. K., Kudose, S., Liu, T., Stappenbeck, T. S., Gaut, J. P., and Swamidass, S. J. (2018). Deep learning global glomerulosclerosis in transplant kidney frozen sections. IEEE Transactions on Medical Imaging, 37(12):2718–2728.
Moura, L. R., Franco, M. F., and Kirsztajn, G. M. (2015). Minimal change disease and focal segmental glomerulosclerosis in adults: response to steroids and risk of renal failure. Brazilian Journal of Nephrology, 37(4):475–480.
Ojala, T., Pietikäinen, M., and Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1):51–59.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3):211–252.
Sheehan, S. M. and Korstanje, R. (2018). Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning. American Journal of Physiology-Renal Physiology, 315(6):F1644–F1651.
Silva, A. C., Carvalho, P. C. P., and Gattass, M. (2004). Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images. Pattern Analysis and Applications, 7(3):227–234.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Barros, G. O., Navarro, B., Duarte, A., and Dos-Santos, W. L. (2017). Pathospotter-k: A computational tool for the automatic identification of glomerular lesions in histological images of kidneys. Scientific reports, 7:46769.
Chagas, P., Souza, L., Araújo, I., Aldeman, N., Duarte, A., Angelo, M., dos Santos, W. L., and Oliveira, L. (2019). Classification of glomerular hypercellularity using convolutional features and support vector machine. arXiv preprint arXiv:1907.00028.
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. arXiv preprint, pages 1610–02357.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1):37–46.
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3):273– 297.
Cover, T. M. and Thomas, J. A. (1991). Entropy, relative entropy and mutual information. Elements of information theory, 2:1–55.
Ginley, B. G., Tomaszewski, J. E., Jen, K.-Y., Fogo, A., Jain, S., and Sarder, P. (2018). Computational analysis of the structural progression of human glomeruli in diabetic nephropathy. In Proceedings of SPIE Medical Imaging, volume 10581, pages 105810A–1–105810A–6.
Haralick, R. M., Shanmugam, K., et al. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, 3(6):610–621.
He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
Ho, T. K. (1995). Random decision forests. In Proceedings of 3rd International Conference on Document Analysis and Recognition, volume 1, pages 278–282.
Isaaks, E., Srivastava, R., and (Firm), K. (1989). An introduction to Applied Geostatistics. Oxford University Press.
Marsh, J. N., Matlock, M. K., Kudose, S., Liu, T., Stappenbeck, T. S., Gaut, J. P., and Swamidass, S. J. (2018). Deep learning global glomerulosclerosis in transplant kidney frozen sections. IEEE Transactions on Medical Imaging, 37(12):2718–2728.
Moura, L. R., Franco, M. F., and Kirsztajn, G. M. (2015). Minimal change disease and focal segmental glomerulosclerosis in adults: response to steroids and risk of renal failure. Brazilian Journal of Nephrology, 37(4):475–480.
Ojala, T., Pietikäinen, M., and Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1):51–59.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3):211–252.
Sheehan, S. M. and Korstanje, R. (2018). Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning. American Journal of Physiology-Renal Physiology, 315(6):F1644–F1651.
Silva, A. C., Carvalho, P. C. P., and Gattass, M. (2004). Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images. Pattern Analysis and Applications, 7(3):227–234.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Publicado
15/06/2021
Como Citar
SANTOS, Justino Duarte; VERAS, Rodrigo M. S.; SILVA, Romuere R. V..
Geoestatísticas e Deep Features para Diferenciar Glomeruloesclerose Segmentar e Focal de Doença de Lesões Mínimas em Imagens de Biópsia Renal. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 21. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 91-96.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas.2021.16107.
