Segmentação Automática da Próstata em Imagens de Ressonância Magnética utilizando Redes Neurais Convolucionais e Mapa Probabilístico
Resumo
O câncer de próstata é o segundo tipo de câncer mais comum entre os homens e atualmente tem crescido a utilização de exames de imagens da próstata para a prevenção, diagnóstico e tratamento. A segmentação manual da próstata é extremamente demorada e propensa à variabilidade entre diferentes especialistas, o que sugere o desenvolvimento de técnicas automáticas para a segmentação da próstata. Neste trabalho, propomos um método totalmente automático para a segmentação da próstata a partir de imagens de ressonância magnética usando uma técnica de aprendizado profundo e mapa probabilístico. Os resultados experimentais aqui obtidos indicam uma segmentação satisfatória, tendo em vista que obtemos um coeficiente de similaridade de Dice médio de 85,17%.
Referências
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Karimi, D., Samei, G., Shao, Y., and Salcudean, T. (2017). A novel deep learning-based method for prostate segmentation in t2-weighted magnetic resonance imaging.
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Mahapatra, D. and Buhmann, J. M. (2014). Prostate mri segmentation using learned semantic knowledge and graph cuts. IEEE Transactions on Biomedical Engineering, 61(3):756–764.
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.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Smith, K., Clark, K., Bennett, W., Nolan, T., Kirby, J., Wolfsberger, M., Moulton, J., Vendt, B., and Freymann, J. (2015). Data from prostate-3t. DOI: 10.7937/K9/TCIA.2015.QJTV5IL5. Acessado: 01 agosto 2017.
Society, A. C. (2018). About prostate cancer. [link]. Acessado: 23 março 2018.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., and Anguelov, D. (2015). & rabinovich, a.(2015). going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9.
Tian, Z., Liu, L., Zhang, Z., and Fei, B. (2018). Psnet: prostate segmentation on mri based on a convolutional neural network. Journal of Medical Imaging, 5(2):021208.
Yang, X., Rossi, P., Ogunleye, T., Marcus, D. M., Jani, A. B., Mao, H., Curran, W. J., and Liu, T. (2014). Prostate ct segmentation method based on nonrigid registration in ultrasound-guided ct-based hdr prostate brachytherapy. Medical physics, 41(11).
Yang, X., Zhan, S., and Xie, D. (2016). Landmark based prostate mri segmentation via improved level set method. In 2016 IEEE 13th International Conference on Signal Processing (ICSP), pages 29–34.
Zeiler, M. D. A. (2012). An adaptive learning rate method. arxiv preprint. arXiv preprint arXiv:1212.5701.
Zhu, Q., Du, B., Turkbey, B., Choyke, P. L., and Yan, P. (2017). Deeply-supervised cnn for prostate segmentation. In Neural Networks (IJCNN), 2017 International Joint Conference on, pages 178–184. IEEE.
Bloch, N., Madabhushi, A., Huisman, H., et al. (2015). Nci-isbi 2013 challenge: automated segmentation of prostate structures.
Duda, R. (1973). Pattern classification and scene analysis. New-York, London, Sydny, Tronto A Wiley-Interscience Publication.
Gonzalez, R. and Woods, R. (2008). Digital image processing. Pearson, Prentice Hall.
INCA (2018). Instituto nacional do câncer - câncer -tipo - prostata. [link]. Acessado: 23 março 2018.
Karimi, D., Samei, G., Shao, Y., and Salcudean, T. (2017). A novel deep learning-based method for prostate segmentation in t2-weighted magnetic resonance imaging.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.
Li, A., Li, C., Wang, X., Eberl, S., Feng, D. D., and Fulham, M. (2013). Automated segmentation of prostate mr images using prior knowledge enhanced random walker. In Digital Image Computing: Techniques and Applications (DICTA), 2013 International Conference on, pages 1–7. IEEE.
Maan, B. and van der Heijden, F. (2012). Prostate mr image segmentation using 3d active appearance models. MICCAI Grand Challenge: Prostate MR Image Segmentation, 2012.
Mahapatra, D. and Buhmann, J. M. (2014). Prostate mri segmentation using learned semantic knowledge and graph cuts. IEEE Transactions on Biomedical Engineering, 61(3):756–764.
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.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Smith, K., Clark, K., Bennett, W., Nolan, T., Kirby, J., Wolfsberger, M., Moulton, J., Vendt, B., and Freymann, J. (2015). Data from prostate-3t. DOI: 10.7937/K9/TCIA.2015.QJTV5IL5. Acessado: 01 agosto 2017.
Society, A. C. (2018). About prostate cancer. [link]. Acessado: 23 março 2018.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., and Anguelov, D. (2015). & rabinovich, a.(2015). going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9.
Tian, Z., Liu, L., Zhang, Z., and Fei, B. (2018). Psnet: prostate segmentation on mri based on a convolutional neural network. Journal of Medical Imaging, 5(2):021208.
Yang, X., Rossi, P., Ogunleye, T., Marcus, D. M., Jani, A. B., Mao, H., Curran, W. J., and Liu, T. (2014). Prostate ct segmentation method based on nonrigid registration in ultrasound-guided ct-based hdr prostate brachytherapy. Medical physics, 41(11).
Yang, X., Zhan, S., and Xie, D. (2016). Landmark based prostate mri segmentation via improved level set method. In 2016 IEEE 13th International Conference on Signal Processing (ICSP), pages 29–34.
Zeiler, M. D. A. (2012). An adaptive learning rate method. arxiv preprint. arXiv preprint arXiv:1212.5701.
Zhu, Q., Du, B., Turkbey, B., Choyke, P. L., and Yan, P. (2017). Deeply-supervised cnn for prostate segmentation. In Neural Networks (IJCNN), 2017 International Joint Conference on, pages 178–184. IEEE.
Publicado
22/07/2018
Como Citar
FERREIRA, Jonnison L.; DA SILVA, Giovanni L. F.; REIS, Artur B. S.; CAVALCANTE, André B.; SILVA, Aristófanes C.; DE PAIVA, Anselmo C..
Segmentação Automática da Próstata em Imagens de Ressonância Magnética utilizando Redes Neurais Convolucionais e Mapa Probabilístico. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 18. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 129-139.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2018.3683.
