Deep Learning Approach for Automated Estimation of 3D Vertebral Orientation of the Lumbar Spine

Abstract

Lumbar degenerative disc diseases constitute a major contributor to lower back pain. In pursuit of an enhanced understanding of lumbar degenerative pathology and the development of more effective treatment modalities, the application of precise measurement techniques for lumbar segment kinematics is imperative. This study aims to pioneer a novel automated lumbar spine orientation estimation method using deep learning techniques, to facilitate the automatic 2D–3D pre-registration of the lumbar spine during physiological movements, to enhance the efficiency of image registration and the accuracy of spinal segment kinematic measurements. A total of 12 asymptomatic volunteers were enrolled and captured in 2 oblique views with 7 different postures. Images were used for deep learning model development training and evaluation. The model was composed of a segmentation module using Mask R-CNN and an estimation module using ResNet50 architecture with a Squeeze-and-Excitation module. The cosine value of the angle between the prediction vector and the vector of ground truth was used to quantify the model performance. Data from another two prospective recruited asymptomatic volunteers were used to compare the time cost between model-assisted registration and manual registration without a model. The cosine values of vector deviation angles at three axes in the cartesian coordinate system were 0.9667 ± 0.004, 0.9593 ± 0.0047 and 0.9828 ± 0.0025, respectively. The value of the angular deviation between the intermediate vector obtained by utilising the three direction vectors and ground truth was 10.7103 ± 0.7466. Results show the consistency and reliability of the model's predictions across different experiments and axes and demonstrate that our approach significantly reduces the registration time (3.47 ± 0.90 min vs. 8.10 ± 1.60 min, p < 0.001), enhances the efficiency, and expands its broader utilisation of clinical research about kinematic measurements.