Detecção de Tromboembolia Pulmonar utilizando Redes Neurais Convolucionais e Extração de Características
Resumo
Embolia pulmonar é uma das principais causas de morte relacionadas a doenças cardiovasculares no mundo, uma vez que é feito o diagnostico é necessária uma resposta rápida pela equipe médica para salvar o paciente. A principal forma de diagnóstico é pelo exame de tomografia computadorizada e, devido a grande quantidade de dados que o exame gera, algoritmos de deep learning têm mostrado bons resultados em encontrar embolia pulmonar de maneira autônoma. O objetivo deste trabalho é desenvolver uma rede de deep learning capaz de encontrar embolia pulmonar em exames de tomografia computadorizada. Até então, utilizando uma rede inspirada na U-net, o método segmentou trombos atingindo um Dice Score de 0.81 e um IoU de 0.79.Referências
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Tan, M. and Le, Q. V. (2019). Efcientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946.
White, R. H. (2003). The epidemiology of venous thromboembolism. Circulation, 107(23 suppl 1):I–4.
Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K. (2016). Aggregated residual transformations for deep neural networks. arXiv preprint arXiv:1611.05431.
Yang, X., Lin, Y., Su, J., Wang, X., Li, X., Lin, J., and Cheng, K.-T. (2019). A two-stage convolutional neural network for pulmonary embolism detection from ctpa images. IEEE Access, 7:84849–84857.
Calder, K. K., Herbert, M., and Henderson, S. O. (2005). The mortality of untreated pulmonary embolism in emergency department patients. Annals of emergency medicine, 45(3):302–310.
Cano-Espinosa, C., Cazorla, M., and González, G. (2020). Computer aided detection of pulmonary embolism using multi-slice multi-axial segmentation. Applied Sciences, 10(8):2945.
Carson, J. L., Kelley, M. A., Duff, A., Weg, J. G., Fulkerson, W. J., Palevsky, H. I., Schwartz, J. S., Thompson, B. T., Popovich Jr, J., Hobbins, T. E., et al. (1992). The clinical course of pulmonary embolism. New England Journal of Medicine, 326(19):1240–1245.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee.
Goldhaber, S. Z. and Bounameaux, H. (2012). Pulmonary embolism and deep vein thrombosis. The Lancet, 379(9828):1835–1846.
González, G., Jimenez-Carretero, D., Rodríguez-López, S., Cano-Espinosa, C., Cazorla, M., Agarwal, T., Agarwal, V., Tajbakhsh, N., Gotway, M. B., Liang, J., et al. (2020). Computer aided detection for pulmonary embolism challenge (cad-pe). arXiv preprint arXiv:2003.13440.
Huang, S.-C., Kothari, T., Banerjee, I., Chute, C., Ball, R. L., Borus, N., Huang, A., Patel, B. N., Rajpurkar, P., Irvin, J., et al. (2020). Penet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric ct imaging. npj Digital Medicine, 3(1):1–9.
Kligerman, S. J., Mitchell, J. W., Sechrist, J. W., Meeks, A. K., Galvin, J. R., and White, C. S. (2018). Radiologist performance in the detection of pulmonary embolism. Journal of thoracic imaging, 33(6):350–357.
Long, K., Tang, L., Pu, X., Ren, Y., Zheng, M., Gao, L., Song, C., Han, S., Zhou, M., and Deng, F. (2021). Probability-based mask r-cnn for pulmonary embolism detection. Neurocomputing, 422:345–353.
Masoudi, M., Pourreza, H.-R., Saadatmand-Tarzjan, M., Eftekhari, N., Zargar, F. S., and Rad, M. P. (2018). A new dataset of computed-tomography angiography images for computer-aided detection of pulmonary embolism. Scientic data, 5:180180.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019). Pytorch: An imperative style, high-performance deep learning library. In Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., and Garnett, R., editors, Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc.
Rajan, D., Beymer, D., Abedin, S., and Dehghan, E. (2020). Pi-pe: A pipeline for pulmonary embolism detection using sparsely annotated 3d ct images. In Machine Learning for Health Workshop, pages 220–232.
Real, E., Aggarwal, A., Huang, Y., and Le, Q. V. (2019). Regularized evolution for image classier architecture search. In Proceedings of the aaai conference on articial intelligence, volume 33, pages 4780–4789.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer.
Sadigh, G., Kelly, A. M., and Cronin, P. (2011). Challenges, controversies, and hot topics in pulmonary embolism imaging. American Journal of Roentgenology, 196(3):497– 515.
Seeram, E. (2018). Computed tomography: A technical review. Radiologic technology, 89(3):279CT–302CT.
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. A. (2017). Inception-v4, inceptionresnet and the impact of residual connections on learning. In Thirty-rst AAAI conference on articial intelligence.
Tajbakhsh, N., Gotway, M. B., and Liang, J. (2015). Computer-aided pulmonary embolism detection using a novel vessel-aligned multi-planar image representation and convolutional neural networks. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 62–69. Springer.
Tajbakhsh, N., Shin, J. Y., Gotway, M. B., and Liang, J. (2019). Computer-aided detection and visualization of pulmonary embolism using a novel, compact, and discriminative image representation. Medical image analysis, 58:101541.
Tan, M. and Le, Q. V. (2019). Efcientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946.
White, R. H. (2003). The epidemiology of venous thromboembolism. Circulation, 107(23 suppl 1):I–4.
Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K. (2016). Aggregated residual transformations for deep neural networks. arXiv preprint arXiv:1611.05431.
Yang, X., Lin, Y., Su, J., Wang, X., Li, X., Lin, J., and Cheng, K.-T. (2019). A two-stage convolutional neural network for pulmonary embolism detection from ctpa images. IEEE Access, 7:84849–84857.
Publicado
15/06/2021
Como Citar
OLESCKI, Gabriel; ANDRADE, João Mario Clementin de; ESCUISSATO, Dante; OLIVEIRA, Lucas Ferrari de.
Detecção de Tromboembolia Pulmonar utilizando Redes Neurais Convolucionais e Extração de Características. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 21. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 381-391.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2021.16081.
