Cobb Angle Measurement Method of Scoliosis Based on U-net Network

Abstract

Abstract The Cobb angle is an important indicator for judging the severity of scoliosis. However, the segmentation and corner marking methods based on deep learning have problems such as target area segmentation and corner detection blur in the X-ray Cobb angle measurement. In this paper, a new convex hull algorithm to detect the corners and a mask generation strategy are proposed to improve the accuracy of Cobb angle recognition. On this basis, the Cobb angle measurement method is presented to identify and segment the target area based on U-net network, and then combine the new convex hull algorithm to detect corners and mask generation strategies. A total of 68 corner points were marked on 17 vertebrae, and the corner points detected by the markers were used to calculate the Cobb angle. The experimental results have proved that the U-net based measurement method could effectively improve the corner detection accuracy on the basis of segmentation, thereby reducing the calculation error of the Cobb angle. The Cobb mean absolute error (AMAE) is 9.2832°, and the symmetric mean absolute percentage error (SMAPE) is 21.675%, which achieved a relatively good result compared with the measurement by professional orthopaedist in error.