Segmentação de Vértebras e Diagnóstico de Fraturas em Imagens de Ressonância Magnética Utilizando U-Net 3D e Deep Belief Network
Resumo
A dor lombar é uma razão comum para visitas clı́nicas e o exame de ressonância magnética é frequentemente utilizado em sistemas de apoio a di- agnóstico de patologias na coluna. Visando aprimorar e automatizar esse pro- cesso, este estudo propõe o uso de técnicas computacionais para a segmentação de vértebras em imagens de ressonância magnética, com o objetivo de realizar posteriores análises acerca de patologias na coluna. Para este fim, são utili- zadas duas arquiteturas de Deep Learning: a U-Net para a segmentação em 3D e a Deep Belief Network para a classificação de vértebras que apresen- tam ruptura ou não. Os resultados obtidos mostram que a U-Net é promissora em localizar a região da vértebra, obtendo um valor de Coeficiente de Dice médio de 89,51%, superando assim vários trabalhos importantes focados no problema. A classificação também se mostrou eficiente, com valores de 94,38% para acurácia e 88,8% de sensibilidade.
Referências
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., and Erickson, B. J. (2017). Deep learning for brain mri segmentation: state of the art and future directions. Journal of digital imaging, 30(4):449–459. http://dx.doi.org/10.1007/s10278-017-9983-4
ANA (2019). Spine disorders, deformities and diseases. https://ana-neurosurgery.com/areas-of-expertise/spine-disorders-deformities-diseases/. Acesso em: 15 de fevereiro de 2019.
Brooks, R. A. (1977). A quantitative theory of the hounsfield unit and its application to dual energy scanning. Journal of computer assisted tomography, 1(4):487–493.
Chu, C., Belavỳ, D. L., Armbrecht, G., Bansmann, M., Felsenberg, D., and Zheng, G. (2015). Fully automatic localization and segmentation of 3d vertebral bodies from ct/mr images via a learning based method. PloS one, 10(11):e0143327. http://dx.doi.org/10.1371/journal.pone.0143327
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
Egger, J., Nimsky, C., and Chen, X. (2017). Vertebral body segmentation with grow-cut: Initial experience, workflow and practical application. SAGE open medicine, 5:2050312117740984. http://dx.doi.org/10.1177/2050312117740984
Freburger, J. K., Holmes, G. M., Agans, R. P., Jackman, A. M., Darter, J. D., Wallace, A. S., Castel, L. D., Kalsbeek, W. D., and Carey, T. S. (2009). The rising prevalence of chronic low back pain. Archives of internal medicine, 169(3):251–258. http://dx.doi.org/10.1001/archinternmed.2008.543
Hille, G., Saalfeld, S., Serowy, S., and Tönnies, K. (2018). Vertebral body segmentation in wide range clinical routine spine mri data. Computer methods and programs in biomedicine, 155:93–99. http://dx.doi.org/10.1016/j.cmpb.2017.12.013
Juntu, J., Sijbers, J., Van Dyck, D., and Gielen, J. (2005). Bias field correction for mri images. In Computer Recognition Systems, pages 543–551. Springer. http://dx.doi.org/10.1007/3-540-32390-2_64
Korez, R., Likar, B., Pernuš, F., and Vrtovec, T. (2016). Model-based segmentation of vertebral bodies from mr images with 3d cnns. In Ourselin, S., Joskowicz, L., Sabuncu, M. R., Unal, G., and Wells, W., editors, Medical Image Computing and Computer- Assisted Intervention – MICCAI 2016, pages 433–441, Cham. Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-46723-8_50
LeCun, Y. and Cortes, C. (2010). MNIST handwritten digit database.
Lenchik, L., Rogers, L. F., Delmas, P. D., and Genant, H. K. (2004). Diagnosis of osteoporotic vertebral fractures: importance of recognition and description by radiologists. American Journal of Roentgenology, 183(4):949–958. http://dx.doi.org/10.2214/ajr.183.4.1830949
Liu, G., Xiao, L., and Xiong, C. (2017). Image classification with deep belief networks and improved gradient descent. In 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), volume 1, pages 375–380. http://dx.doi.org/10.1109/CSE-EUC.2017.74
Lu, J.-T., Pedemonte, S., Bizzo, B., Doyle, S., Andriole, K. P., Michalski, M. H., Gonzalez, R. G., and Pomerantz, S. R. (2018). Deepspine: Automated lumbar vertebral segmentation, disc-level designation, and spinal stenosis grading using deep learning. arXiv preprint arXiv:1807.10215.
Mendieta, J. B. (2016). An efficient and semiautomatic segmentation method for 3d surface reconstruction of the lumbar spine from magnetic resonance imaging (mri). Master’s thesis, Queensland University of Technology.
Neubert, A., Fripp, J., Shen, K., Salvado, O., Schwarz, R., Lauer, L., Engstrom, C., and Crozier, S (2011). Automated 3d segmentation of vertebral bodies and intervertebral discs from mri. In 2011 International Conference on Digital Image Computing: Techniques and Applications, pages 19–24. http://dx.doi.org/10.1109/DICTA.2011.12
Richards, P. J., George, J., Metelko, M., and Brown, M. (2010). Spine computed tomography doses and cancer induction. Spine, 35(4):430–433. http://dx.doi.org/10.1097/BRS.0b013e3181cdde47
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. http://dx.doi.org/10.1007/978-3-319-24574-4_28
Schwarzenberg, R., Freisleben, B., Nimsky, C., and Egger, J. (2014). Cube-cut: vertebral body segmentation in mri-data through cubic-shaped divergences. PloS one, 9(4):e93389. http://dx.doi.org/10.1371/journal.pone.0093389
Zou, K. H., Warfield, S. K., Bharatha, A., Tempany, C. M., Kaus, M. R., Haker, S. J., Wells III, W. M., Jolesz, F. A., and Kikinis, R. (2004). Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports. Academic radiology, 11(2):178–189. http://dx.doi.org/10.1016/S1076-6332(03)00671-8
Zukić, D., Vlasák, A., Egger, J., Hořı́nek, D., Nimsky, C., and Kolb, A. (2014). Robust detection and segmentation for diagnosis of vertebral diseases using routine mr images. In Computer Graphics Forum, volume 33, pages 190–204. Wiley Online Library. http://dx.doi.org/10.1111/cgf.12343
ANA (2019). Spine disorders, deformities and diseases. https://ana-neurosurgery.com/areas-of-expertise/spine-disorders-deformities-diseases/. Acesso em: 15 de fevereiro de 2019.
Brooks, R. A. (1977). A quantitative theory of the hounsfield unit and its application to dual energy scanning. Journal of computer assisted tomography, 1(4):487–493.
Chu, C., Belavỳ, D. L., Armbrecht, G., Bansmann, M., Felsenberg, D., and Zheng, G. (2015). Fully automatic localization and segmentation of 3d vertebral bodies from ct/mr images via a learning based method. PloS one, 10(11):e0143327. http://dx.doi.org/10.1371/journal.pone.0143327
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
Egger, J., Nimsky, C., and Chen, X. (2017). Vertebral body segmentation with grow-cut: Initial experience, workflow and practical application. SAGE open medicine, 5:2050312117740984. http://dx.doi.org/10.1177/2050312117740984
Freburger, J. K., Holmes, G. M., Agans, R. P., Jackman, A. M., Darter, J. D., Wallace, A. S., Castel, L. D., Kalsbeek, W. D., and Carey, T. S. (2009). The rising prevalence of chronic low back pain. Archives of internal medicine, 169(3):251–258. http://dx.doi.org/10.1001/archinternmed.2008.543
Hille, G., Saalfeld, S., Serowy, S., and Tönnies, K. (2018). Vertebral body segmentation in wide range clinical routine spine mri data. Computer methods and programs in biomedicine, 155:93–99. http://dx.doi.org/10.1016/j.cmpb.2017.12.013
Juntu, J., Sijbers, J., Van Dyck, D., and Gielen, J. (2005). Bias field correction for mri images. In Computer Recognition Systems, pages 543–551. Springer. http://dx.doi.org/10.1007/3-540-32390-2_64
Korez, R., Likar, B., Pernuš, F., and Vrtovec, T. (2016). Model-based segmentation of vertebral bodies from mr images with 3d cnns. In Ourselin, S., Joskowicz, L., Sabuncu, M. R., Unal, G., and Wells, W., editors, Medical Image Computing and Computer- Assisted Intervention – MICCAI 2016, pages 433–441, Cham. Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-46723-8_50
LeCun, Y. and Cortes, C. (2010). MNIST handwritten digit database.
Lenchik, L., Rogers, L. F., Delmas, P. D., and Genant, H. K. (2004). Diagnosis of osteoporotic vertebral fractures: importance of recognition and description by radiologists. American Journal of Roentgenology, 183(4):949–958. http://dx.doi.org/10.2214/ajr.183.4.1830949
Liu, G., Xiao, L., and Xiong, C. (2017). Image classification with deep belief networks and improved gradient descent. In 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), volume 1, pages 375–380. http://dx.doi.org/10.1109/CSE-EUC.2017.74
Lu, J.-T., Pedemonte, S., Bizzo, B., Doyle, S., Andriole, K. P., Michalski, M. H., Gonzalez, R. G., and Pomerantz, S. R. (2018). Deepspine: Automated lumbar vertebral segmentation, disc-level designation, and spinal stenosis grading using deep learning. arXiv preprint arXiv:1807.10215.
Mendieta, J. B. (2016). An efficient and semiautomatic segmentation method for 3d surface reconstruction of the lumbar spine from magnetic resonance imaging (mri). Master’s thesis, Queensland University of Technology.
Neubert, A., Fripp, J., Shen, K., Salvado, O., Schwarz, R., Lauer, L., Engstrom, C., and Crozier, S (2011). Automated 3d segmentation of vertebral bodies and intervertebral discs from mri. In 2011 International Conference on Digital Image Computing: Techniques and Applications, pages 19–24. http://dx.doi.org/10.1109/DICTA.2011.12
Richards, P. J., George, J., Metelko, M., and Brown, M. (2010). Spine computed tomography doses and cancer induction. Spine, 35(4):430–433. http://dx.doi.org/10.1097/BRS.0b013e3181cdde47
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. http://dx.doi.org/10.1007/978-3-319-24574-4_28
Schwarzenberg, R., Freisleben, B., Nimsky, C., and Egger, J. (2014). Cube-cut: vertebral body segmentation in mri-data through cubic-shaped divergences. PloS one, 9(4):e93389. http://dx.doi.org/10.1371/journal.pone.0093389
Zou, K. H., Warfield, S. K., Bharatha, A., Tempany, C. M., Kaus, M. R., Haker, S. J., Wells III, W. M., Jolesz, F. A., and Kikinis, R. (2004). Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports. Academic radiology, 11(2):178–189. http://dx.doi.org/10.1016/S1076-6332(03)00671-8
Zukić, D., Vlasák, A., Egger, J., Hořı́nek, D., Nimsky, C., and Kolb, A. (2014). Robust detection and segmentation for diagnosis of vertebral diseases using routine mr images. In Computer Graphics Forum, volume 33, pages 190–204. Wiley Online Library. http://dx.doi.org/10.1111/cgf.12343
Publicado
11/06/2019
Como Citar
PAIVA, Anderson Matheus Passos; DINIZ, João Otávio Bandeira; SILVA, Aristófanes Corrêa; PAIVA, Anselmo Cardoso.
Segmentação de Vértebras e Diagnóstico de Fraturas em Imagens de Ressonância Magnética Utilizando U-Net 3D e Deep Belief Network. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 19. , 2019, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 106-117.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2019.6246.
