Vertebral Center Points Locating and Cobb Angle Measurement Based on Deep Learning

Abstract

Traditional manual measurement of Cobb angle is a time-consuming process and leads to different results. To address this issue, this paper proposes a deep learning-based method of locating the vertebral center points. The whole X-ray can be input into the network for prediction, without worrying about the detection of cervical vertebrae with similar characteristics to the thoracic and lumbar vertebrae. First, key points predicting and noise points filtering operations are employed to obtain vertebral center points for fitting. Then, the spine curve is fitted, and the slope of the normal line of the spine curve is adjusted according to an empirical formula. Finally, the Cobb angle allowed by the error is calculated. Through the reliability analysis of the traditional manual measurement method and the automatic detection method in this paper, ICC (intraclass correlation coefficient) with the two observers was 0.897 and 0.901, respectively, and MAD (mean absolute deviation) was 3.13° and 3.04° respectively. This indicates that the automatic detecting method by computer has good reliability. Therefore, this method can be used to detect scoliosis quickly and effectively.