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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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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