Deep Learning Based Algorithm For Automatic Scoliosis Angle Measurement

Abstract

Scoliosis is a common back disease which identifies with an irregular spinal condition. In this case, the spine has a side curvature with an angle. Practically, the standard angle estimation method is done by measuring the Cobb angle for the curvature. Cobb angle is the angle between two drawn lines, upper-end line and lower-end line of the curve. However, manual measurement needs time and effort. In this paper, we proposed an automatic measurement algorithm with machine learning. Initially, X-Rays images are processed utilizing CLAHE method. Then, deep convolutional neural networks (CNN) are applied to detect vertebrae in each X-Ray image. At last, the Cobb angle is measured through a novel algorithm using trigonometry. The proposed method is evaluated on X-Rays dataset from King Saud University (KSU), and it detects each vertebra in those images. In addition, Cobb angle measurements are compared with experts’ manual measurements. Our method achieves the estimation of Cobb angles with high accuracy, showing its great potential in clinical use.