A Projective-Geometry-Aware Network for 3D Vertebra Localization in Calibrated Biplanar X-Ray Images

Current Deep Learning (DL)-based methods for vertebra localization in biplanar X-ray images mainly focus on two-dimensional (2D) information and neglect the projective geometry, limiting the accuracy of 3D navigation in X-ray-guided spine surgery. A 3D vertebra localization method from calibrated biplanar X-ray images is highly desired to address the problem. In this study, a projective-geometry-aware network for localizing 3D vertebrae in calibrated biplanar X-ray images, referred to as ProVLNet, is proposed. The network design of ProVLNet features three components: a Siamese 2D feature extractor to extract local appearance features from the biplanar X-ray images, a spatial alignment fusion module to incorporate the projective geometry in fusing the extracted 2D features in 3D space, and a 3D landmark regression module to regress the 3D coordinates of the vertebrae from the 3D fused features. Evaluated on two typical and challenging datasets acquired from the lumbar and the thoracic spine, ProVLNet achieved an identification rate of 99.53% and 98.98% and a point-to-point error of 0.64 mm and 1.38 mm, demonstrating superior performance of our proposed approach over the state-of-the-art (SOTA) methods.