Localization of Vertebrae and Deformity Analysis using Digital Spinal Cord Images

Abstract

The spinal cord is an fundamental structure that creates a connection between the brain and the rest of the body. The long thin cord is made up of twisted nerves and tissues in combination with the 33 separate bones stacked over one another. Curvature deformity causes an extra bend in the spinal curve. The curvature deformities are of three types Kyphosis (thoracic region); Lordosis (cervical and lumbar region) , and Scoliosis (sideways). Different imaging techniques clinically used to diagnose these deformities include X-Ray, Computed Tomography and Magnetic Resonance Imaging. Many researchers have worked on deformity analysis of spinal curvatures, and numerous competitions and workshops have produced labeled datasets and new approaches as well. Recently, few semi-automated systems have been proposed for vertebrae segmentation and Scoliosis Cobb estimation but a fully automated method that can differentiate all three categories, and identify severity levels among the disorders with multiple imaging modalities is still missing. In this research, we present a two-step automated system for localization of vertebrae, segmentation of the spinal column, and classification of diseases on the basis of their curvature shape and Cobb estimation. A recent approach to object detection is utilized for vertebrae localization, in parallel to this spine column is segmented. Both of these results are used for the extraction of the midline curvature profile. These results supported in feature-based shape analysis mechanism for reliable classification of curvature, respectively. The proposed system also involves a traditional Cobb estimation procedure for curvature analysis and validation provides reliability to our predicted results. The evaluation of both modules has been carried out, using available datasets. The localization results achieved mean Average Precision (map) up to 0.94 for AASCE19, 0.97 for the Mendeley’s dataset and 0.95 for the CSI16 dataset. Segmentation of spine column attained dice score up to 0.971, 0.960 and 0.953 for Mendel’s, CSI16 and AASCE19 respectively. The comparison of segmentation block with literature shows improvement in dice score. The results of shape analysis using Random Forest (RF) classifiers attained an accuracy of 94.69%. Considering the same problem as that of image classification, the proposed feature-set performed better as compared with deep features of Efficient-Net B4 with a 2% improvement in the accuracy. The Cobb estimation results in comparison with latest state-of-the-art reduced the Mean Absolute Error (MAE) by 2 degrees. The classification of Lumbar Lordosis on the basis of proposed methodology achieved an accuracy up to 98.04% for Mendeley’s dataset and 81.25% for CSI16 dataset respectively.