Automatic segmentation of brain lesions in magnetic resonance imaging using superpixel, PSO and the generator with auxiliary classifier
Abstract
Gliomas are one of the most severe brain tumors. However, manual targeting is a difficult and time-consuming task. Therefore, this work proposes an automatic method for the segmentation of sub-regions of lesions in the brain in 3D MR images based on superpixels, PSO algorithm and the auxiliary generator network with auxiliary classifier. The proposed method obtained results for necrosis, edema, solid nucleus and nucleus, an accuracy of 67.71 %, 94.57 %, 18.44 %, 89.35 % in the classification stage and coefficient dice of 60.35 %, 44.22 %, 16.45 %, 31.23 % in the segmentation stage for the respective subregions. The results demonstrate the difficulty in the classification and segmentation of the tumor sub-regions.
References
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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.
Published
2019-06-11
How to Cite
CIPRIANO, Carolina L. S.; DA SILVA , Giovanni L. F.; FERREIRA, Jonnison L. ; SILVA, Aristófanes C.; DE PAIVA, Anselmo Cardoso.
Automatic segmentation of brain lesions in magnetic resonance imaging using superpixel, PSO and the generator with auxiliary classifier. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (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.
