Segmentação automática de lesões no cérebro em imagens de ressonância magnética usando superpixel, PSO e a rede geradora adversária com classificador auxiliar
Resumo
Gliomas são um dos tumores cerebrais mais severos. No entanto, a segmentação manual é uma tarefa difı́cil e demorada. Portanto, esse trabalho propõe um método automático para a segmentação de sub-regiões de lesões no cérebro em imagens de RM 3D baseado em superpixels, algoritmo PSO e a rede geradora adversária com classificador auxiliar. O método proposto obteve como resultados para as sub-regiões necrose, edema, núcleo sólido e núcleo, uma acurácia de 67,71%, 94,57%, 18,44%, 89,35% na etapa de classificação e coeficiente dice de 60,35%, 44,22%, 16,45%, 31,23% na etapa de segmentação para as respectivas sub-regiões. Os resultados demonstram a dificuldade na classificação e segmentação das sub-regiões tumorais.
Referências
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Silva, G. L. F. d., Valente, T. L. A., Silva, A. C., de Paiva, A. C., and Gattass, M. (2018). Convolutional neural network-based pso for lung nodule false positive reduction on ct images. Computer methods and programs in biomedicine, 162:109–118. http://dx.doi.org/10.1016/j.cmpb.2018.05.006
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Alex, V., KP, M. S., Chennamsetty, S. S., and Krishnamurthi, G. (2017). Generative adversarial networks for brain lesion detection. In Medical Imaging 2017: Image Processing, volume 10133, page 101330G. International Society for Optics and Photonics. http://dx.doi.org/10.1117/12.2254487
Dice, L. R. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3):297–302. http://dx.doi.org/10.2307/1932409
Diniz, P. H. B., Azevedo Valente, T. L., Bandeira Diniz, J. O., Silva, A. C., Gattass, M., Ventura, N., Muniz, B. C., and Gasparetto, E. L. (2018). Detection of white matter lesion regions in mri using slico and convolutional neural network. Computer methods and programs in biomedicine, 167:49 63. http://dx.doi.org/10.1016/j.cmpb.2018.04.011
Eberhart, R. and Kennedy, J. (1995). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS’95., Proceedings of the Sixth International Symposium on, pages 39–43. IEEE. http://dx.doi.org/10.1109/MHS.1995.494215
Goodenberger, M. L. and Jenkins, R. B. (2012). Genetics of adult glioma. Cancer genetics, 205(12):613–621. http://dx.doi.org/10.1016/j.cancergen.2012.10.009
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems, pages 2672–2680.
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., and Larochelle, H. (2017). Brain tumor segmentation with deep neural networks. Medical image analysis, 35:18–31.
INCA (2019).Instituto nacional do câncer, tipos de câncer: Cérebro. https://www.inca.gov.br/tipos-de-cancer/ cancer-do-sistema-nervoso-central. Accessed: 2019-01-13.
Işın, A., Direkoğlu, C., and Şah, M. (2016). Review of mri-based brain tumor image segmentation using deep learning methods. Procedia Computer Science, 102:317– 324. http://dx.doi.org/10.1016/j.procs.2016.09.407
Lefkovits, L., Lefkovits, S., and Vaida, M.-F. (2016). Brain tumor segmentation based on random forest. Memoirs of the Scientific Sections of the Romanian Academy, 39(1):83– 93.
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015). The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging, 34(10):1993. http://dx.doi.org/10.1109/TMI.2014.2377694
Morris, S. A. and Slesnick, T. C. (2018). Magnetic resonance imaging. Visual Guide to Neonatal Cardiology, pages 104–108.
Odena, A., Olah, C., and Shlens, J. (2017). Conditional image synthesis with auxiliary classifier gans. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pages 2642–2651. JMLR. org.
Omuro, A. and DeAngelis, L. M. (2013). Glioblastoma and other malignant gliomas: a clinical review. Jama, 310(17):1842–1850. http://dx.doi.org/10.1001/jama.2013.280319
Pereira, S., Pinto, A., Alves, V., and Silva, C. A. (2016). Brain tumor segmentation using convolutional neural networks in mri images. IEEE transactions on medical imaging, 35(5):1240–1251. http://dx.doi.org/10.1109/TMI.2016.2538465
Siegel, R. L., Miller, K. D., and Jemal, A. (2016). Cancer statistics, 2016. CA: a cancer journal for clinicians, 66(1):7–30. http://dx.doi.org/10.3322/caac.21332
Silva, G. L. F. d., Valente, T. L. A., Silva, A. C., de Paiva, A. C., and Gattass, M. (2018). Convolutional neural network-based pso for lung nodule false positive reduction on ct images. Computer methods and programs in biomedicine, 162:109–118. http://dx.doi.org/10.1016/j.cmpb.2018.05.006
Stewart, B., Wild, C. P., et al. (2014). World cancer report 2014.
Publicado
11/06/2019
Como Citar
CIPRIANO, Carolina L. S.; DA SILVA , Giovanni L. F.; FERREIRA, Jonnison L. ; SILVA, Aristófanes C.; DE PAIVA, Anselmo Cardoso.
Segmentação automática de lesões no cérebro em imagens de ressonância magnética usando superpixel, PSO e a rede geradora adversária com classificador auxiliar. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 19. , 2019, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 199-209.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2019.6254.
