Abstract
Automatic vertebra segmentation is a challenging task from CT images due to anatomically complexity, shape variation and vertebrae articulation with each other. Deep Learning is a machine learning paradigm that focuses on deep hierarchical learning modeling of input data. In this paper, we propose a novel approach of automatic vertebrae segmentation from computed tomography (CT) images by using deep belief networks (BDNs) modeling. Using the DBN model, the contexture features of vertebra from CT images are extracted automatically by an unsupervised pattern called pre-training and followed by supervised training called back-propagation algorithm; then segmentation the vertebra from other abdominal structure. To evaluate the performance, we computed the overall accuracy (94.2%), sensitivity (83.2%), specificity (94.8%) and mean Dice coefficients (0.85 ± 0.03) for segmentation evaluation. Experimental results show that our proposed model provides a more accuracy in vertebra segmentation compared to the previous state of art methods.
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References
Kadoury, S., Labelle, H., Paragios, N.: Spine segmentation in medical images using manifold embeddings and higher-order MRFs. Med. Imaging IEEE Trans. 32(7), 1227–1238 (2013)
Rasoulian, A., Rohling, R., Abolmaesumi, P.: Lumbar spine segmentation using a statistical multi-vertebrae anatomical shape+pose model. Med. Imaging IEEE Trans. 32(10), 1890–1900 (2013)
Aslan, M.S., Farag, A.A., Arnold, B., Xiang, P.: Segmentation of vertebrae using level sets with expectation maximization algorithm. IEEE 2010–2013 (2011)
Ghosh, S., Alomari, R., Chaudhary, V., Dhillon G.: Automatic lumbar vertebrae segmentation from clinical CT for wedge compression fracture diagnosis. In: SPIE, pp. 1–9 (2011)
Narkhede, H.P.: Review of image segmentation techniques. Int. J. Sci. Mod. Eng. 2319–6386 (2013)
Aslan, M.S., Ali, A., Farag, A., Rara, A., Arnold, B., Xiang, P.: 3D vertebrae body segmentation using shape based graph cuts. In: IEEE ICPR, pp. 3951–3954 (2010)
Suzani, A., Rasoulian, A., Seitel, A., Fels, S., Rohling, R.N.: Deep learning for automatic localization, identification, and segmentation of vertebral bodies in volumetric MR images, vol. 9415, pp. 1–7 (2015)
Herring, J., Dawant, B.: Automatic lumbar vertebral identification using surface-based registration. Comput. Biomed. Res. 34(2), 74–84 (2001)
Klinder, T., Wolz, R., Lorenz, C., Franz, A., Ostermann, J.: Spine segmentation using articulated shape models. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5241, pp. 227–234. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85988-8_28
Lim, P.H., Bagci, U., Bai, L.: Introducing willmore flow into level set segmentation of spinal vertebrae. IEEE Trans. Biomed. Eng. 60, 115–122 (2013)
Naegel, B.: Using mathematical morphology for the anatomical labeling of vertebrae from 3D CT-scan images. Comput. Med. Imaging Graph. 31(3), 141–156 (2007)
Ghebreab, S., Smeulders, A.: Combining strings and neck- laces for interactive three-dimensional segmentation of spinal images using an integral deformable spine model. IEEE Trans. Biomed. Eng. 51(10), 1821–1829 (2004)
Hacihaliloglu, I., Rasoulian, A., Rohling, R.N., Abolmaesumi, P.: Local phase tensor features for 3-D ultrasound to statistical shape+pose spine model registration. IEEE Trans. Med. Imaging 33, 2167–2179 (2014)
Hinton, G., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)
Khatami, A., Khosravi, A., Nguyen, T., Lim, C.P., Nahavandi, S.: Medical image analysis using wavelet transform and deep belief networks. Expert Syst. Appl. 86, 190–198 (2017)
Abdel-Zaher, A.M., Eldeib, A.M.: Breast cancer classification using deep belief networks. Expert Syst. Appl. 46, 139–144 (2016)
Sun, W., Zheng, B., Qian, W.: Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput. Biol. Med. 89, 530–539 (2017)
Hatipoglu, N., Bilgin, G.: Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships. Med. Biol. Eng. Comput. 55, 1829–1848 (2017)
Pinaya, W.H.L., et al.: Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia. Sci. Rep. 6, 38897 (2016)
Zhang, S., et al.: A deep learning framework for modeling structural features of RNA-binding protein targets. Nucleic Acids Res. 44, e32 (2015)
Li, H., Li, X., Ramanathan, M., Zhang, A.: Identifying informative risk factors and predicting bone disease progression via deep belief networks. Methods 69, 257–265 (2014)
Yao, J., Burns, J.E., Munoz, H., Summers, R.M.: Detection of vertebral body fractures based on cortical shell unwrapping. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 509–516. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33454-2_63
Ahmad, M., Yang, J., Ai, D., Qadri, S.F., Wang, Y.: Deep-stacked auto encoder for liver segmentation. In: Wang, Y., et al. (eds.) IGTA 2017. CCIS, vol. 757, pp. 243–251. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7389-2_24
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This work was supported by the National Science Foundation Program of China (61527827).
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Qadri, S.F., Ahmad, M., Ai, D., Yang, J., Wang, Y. (2018). Deep Belief Network Based Vertebra Segmentation for CT Images. In: Wang, Y., Jiang, Z., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science, vol 875. Springer, Singapore. https://doi.org/10.1007/978-981-13-1702-6_53
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DOI: https://doi.org/10.1007/978-981-13-1702-6_53
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