A deep learning algorithm for automated measurement of vertebral body compression from X-ray images

• Published in: Scientific reports Vol. 11; no. 1; p. 13732
• Main Authors: Seo, Jae Won, Lim, Sang Heon, Jeong, Jin Gyo, Kim, Young Jae, Kim, Kwang Gi, Jeon, Ji Young
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 02.07.2021; Nature Publishing Group UK; Nature Portfolio
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; Compression; Deep Learning; Diagnosis; Female; Fractures, Compression - diagnosis; Fractures, Compression - diagnostic imaging; Fractures, Compression - pathology; Humans; Image Processing, Computer-Assisted - statistics & numerical data; Male; Mean square errors; Middle Aged; Neural networks; Neural Networks, Computer; Osteoporosis; Segmentation; Spinal Fractures; Tomography, X-Ray Computed - statistics & numerical data; Vertebrae; Vertebral Body - diagnostic imaging; Vertebral Body - pathology
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-93017-x

The vertebral compression is a significant factor for determining the prognosis of osteoporotic vertebral compression fractures and is generally measured manually by specialists. The consequent misdiagnosis or delayed diagnosis can be fatal for patients. In this study, we trained and evaluated the performance of a vertebral body segmentation model and a vertebral compression measurement model based on convolutional neural networks. For vertebral body segmentation, we used a recurrent residual U-Net model, with an average sensitivity of 0.934 (± 0.086), an average specificity of 0.997 (± 0.002), an average accuracy of 0.987 (± 0.005), and an average dice similarity coefficient of 0.923 (± 0.073). We then generated 1134 data points on the images of three vertebral bodies by labeling each segment of the segmented vertebral body. These were used in the vertebral compression measurement model based on linear regression and multi-scale residual dilated blocks. The model yielded an average mean absolute error of 2.637 (± 1.872) (%), an average mean square error of 13.985 (± 24.107) (%), and an average root mean square error of 3.739 (± 2.187) (%) in fractured vertebral body data. The proposed algorithm has significant potential for aiding the diagnosis of vertebral compression fractures.