The measurement of Cobb angle based on spine X-ray images using multi-scale convolutional neural network.

Abstract

Adolescent idiopathic scoliosis (AIS) is a structural spinal deformity mainly in the coronal plane and is among the most frequent deformities in children, adolescents, and young adults, with an overall prevalence of 0.47-5.2%. The Cobb angle is an objective measure to determine the progression of deformity and plays a critical role in the planning of surgical treatment. However, existing studies suggested that Cobb angle measurement is susceptible to inter- and intra-observer variability, as well as a high variability in the definition of the end vertebra. In this study, we proposed an automatic method for the spine vertebrae segmentation using Deeplab V3+, a powerful tool that has shown success in the image segmentation of other anatomical regions but spine, and Cobb angle measurement. The segmentation performance was compared to existing mainstay neural networks. Compared to U-Net, Residual U-Net and Dilated U-Net, our method using Deeplab V3+ showed the best performance in the Dice Similarity Coefficient (DSC), accuracy, sensitivity and Jaccard Index. An excellent correlation in the final Cobb angle calculation was achieved between the smallest distance point (SDP) method and two experts (> 0.95), with a small error in the angle estimation compared (MAE < 3°). The proposed method could provide a potential tool for the automatic estimation of the Cobb angle to improve the efficiency and accuracy of the treatment workflow for AIS.