Desenvolvimento de um Sistema CAD para Detecção de Nódulo Pulmonar em Exames de Tomografia Computadorizada de Tórax
Resumo
O câncer de pulmão tem atraído a atenção das agências de saúde em razão da alta taxa de mortalidade no mundo. Porém, realizar o diagnóstico de câncer de pulmão não é uma tarefa trivial. Para auxiliar o diagnóstico, o exame de Tomografia Computadorizada (CT) tem sido utilizado. Diante desse cenário, pesquisadores têm desenvolvido sistemas de Diagnóstico Auxiliado por Computador (CAD). Motivada por essa problemática, este trabalho apresenta uma metodologia para detecção de nódulo pulmonar em exame de CT de tórax. Para validar a metodologia proposta, um sistema CAD foi implementado. O sistema foi avaliado com os dados e métricas do desafio LUNA16, e obteve sensibilidade de 94, 90% com a taxa média de 1, 00 falso positivo por exame.
Referências
Doi, K. (2007). Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 31(4):198–211. Computer-aided Diagnosis (CAD) and Image-guided Decision Support.
Dou, Q., Chen, H., Yu, L., Qin, J., and Heng, P.-A. (2017). Multilevel contextual 3-d cnns for false positive reduction in pulmonary nodule detection. IEEE Transactions on Biomedical Engineering, 64(7):1558–1567.
Gong, L., Jiang, S., Yang, Z., Zhang, G., and Wang, L. (2019). Automated pulmonary nodule detection in CT images using 3d deep squeeze-and-excitation networks. International Journal of Computer Assisted Radiology and Surgery, 14(11):1969–1979.
Halder, A., Dey, D., and Sadhu, A. K. (2020). Lung nodule detection from feature engineering to deep learning in thoracic CT images: a comprehensive review. Journal of Digital Imaging, 33(3):655–677.
He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017). Mask r-cnn. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2980–2988.
Khosravan, N. and Bagci, U. (2018). S4nd: Single-shot single-scale lung nodule detection. In Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-López, C., and Fichtinger, G., editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, pages 794–802, Cham. Springer International Publishing.
Li,W., Cao, P., Zhao, D., and Wang, J. (2016). Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Computational and Mathematical Methods in Medicine, 2016:1–7.
Pereira, F., Menotti, D., and Ferrari de Oliveira, L. (2019). A 3d lung nodule candidate detection by grouping dcnn 2d candidates. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,, pages 537–544. INSTICC, SciTePress.
Pereira, F. R., De Andrade, J. M. C., Escuissato, D. L., and De Oliveira, L. F. (2021). Classifier ensemble based on computed tomography attenuation patterns for computeraided detection system. IEEE Access, 9:123134–123145.
Setio, A. A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., van Riel, S. J., Wille, M. M. W., Naqibullah, M., Sánchez, C. I., and van Ginneken, B. (2016). Pulmonary nodule detection in ct images: False positive reduction using multi-view convolutional networks. IEEE Transactions on Medical Imaging, 35(5):1160–1169.
Tang, H., Zhang, C., and Xie, X. (2019). Nodulenet: Decoupled false positive reduction for pulmonary nodule detection and segmentation. In Shen, D., Liu, T., Peters, T. M., Staib, L. H., Essert, C., Zhou, S., Yap, P.-T., and Khan, A., editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, pages 266–274, Cham. Springer International Publishing.
Valente, I. R. S., Cortez, P. C., Neto, E. C., Soares, J. M., de Albuquerque, V. H. C., and Tavares, J. M. R. (2016). Automatic 3d pulmonary nodule detection in CT images: A survey. Computer Methods and Programs in Biomedicine, 124:91–107.
World Health Organization (2018). Cancer. http://www.who.int/mediacentre/factsheets/fs297/en/. Online; Accessed: 2020-11-20.
Zhang, G., Jiang, S., Yang, Z., Gong, L., Ma, X., Zhou, Z., Bao, C., and Liu, Q. (2018). Automatic nodule detection for lung cancer in ct images: A review. Computers in Biology and Medicine, 103:287–300.
Dou, Q., Chen, H., Yu, L., Qin, J., and Heng, P.-A. (2017). Multilevel contextual 3-d cnns for false positive reduction in pulmonary nodule detection. IEEE Transactions on Biomedical Engineering, 64(7):1558–1567.
Gong, L., Jiang, S., Yang, Z., Zhang, G., and Wang, L. (2019). Automated pulmonary nodule detection in CT images using 3d deep squeeze-and-excitation networks. International Journal of Computer Assisted Radiology and Surgery, 14(11):1969–1979.
Halder, A., Dey, D., and Sadhu, A. K. (2020). Lung nodule detection from feature engineering to deep learning in thoracic CT images: a comprehensive review. Journal of Digital Imaging, 33(3):655–677.
He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017). Mask r-cnn. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2980–2988.
Khosravan, N. and Bagci, U. (2018). S4nd: Single-shot single-scale lung nodule detection. In Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-López, C., and Fichtinger, G., editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, pages 794–802, Cham. Springer International Publishing.
Li,W., Cao, P., Zhao, D., and Wang, J. (2016). Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Computational and Mathematical Methods in Medicine, 2016:1–7.
Pereira, F., Menotti, D., and Ferrari de Oliveira, L. (2019). A 3d lung nodule candidate detection by grouping dcnn 2d candidates. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,, pages 537–544. INSTICC, SciTePress.
Pereira, F. R., De Andrade, J. M. C., Escuissato, D. L., and De Oliveira, L. F. (2021). Classifier ensemble based on computed tomography attenuation patterns for computeraided detection system. IEEE Access, 9:123134–123145.
Setio, A. A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., van Riel, S. J., Wille, M. M. W., Naqibullah, M., Sánchez, C. I., and van Ginneken, B. (2016). Pulmonary nodule detection in ct images: False positive reduction using multi-view convolutional networks. IEEE Transactions on Medical Imaging, 35(5):1160–1169.
Tang, H., Zhang, C., and Xie, X. (2019). Nodulenet: Decoupled false positive reduction for pulmonary nodule detection and segmentation. In Shen, D., Liu, T., Peters, T. M., Staib, L. H., Essert, C., Zhou, S., Yap, P.-T., and Khan, A., editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, pages 266–274, Cham. Springer International Publishing.
Valente, I. R. S., Cortez, P. C., Neto, E. C., Soares, J. M., de Albuquerque, V. H. C., and Tavares, J. M. R. (2016). Automatic 3d pulmonary nodule detection in CT images: A survey. Computer Methods and Programs in Biomedicine, 124:91–107.
World Health Organization (2018). Cancer. http://www.who.int/mediacentre/factsheets/fs297/en/. Online; Accessed: 2020-11-20.
Zhang, G., Jiang, S., Yang, Z., Gong, L., Ma, X., Zhou, Z., Bao, C., and Liu, Q. (2018). Automatic nodule detection for lung cancer in ct images: A review. Computers in Biology and Medicine, 103:287–300.
Publicado
07/06/2022
Como Citar
PEREIRA, Fernando Roberto; OLIVEIRA, Lucas Ferrari de.
Desenvolvimento de um Sistema CAD para Detecção de Nódulo Pulmonar em Exames de Tomografia Computadorizada de Tórax. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 22. , 2022, Teresina/PI.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 19-24.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas_estendido.2022.222539.
