3DSRNet: 3-D Spine Reconstruction Network Using 2-D Orthogonal X-Ray Images Based on Deep Learning
Yuan Gao, Hui Tang, Rongjun Ge, Jin Liu, Xin Chen, Yan Xi, Xu Ji, Huazhong Shu, Zhu Jian, Gouenou Coatrieux, Jean-Louis Coatrieux, Yang Chen
Abstract
Orthopedic spine disease is one of the most common diseases in the clinic. The diagnosis of spinal orthopedic injury is an important basis for the treatment of spinal orthopedic diseases. Due to the complexity of the spine structure, doctors usually need to rely on orthopedic CT image data for accurate diagnosis. In some cases, such as poor areas or in emergency situations, it is difficult for doctors to make accurate diagnoses using only 2D x-ray images due to lack of 3D imaging equipment or time crunch. Therefore, an approach based on 2D x-ray images is needed to solve this problem. In this paper, a novel 3D spine reconstruction technique based on 2D orthogonal x-ray images (3DSRNet) is designed. 3DSRNet uses a generative adversarial network architecture and novel modules to make 3D spine reconstruction more accurate and efficient. Spine reconstruction CNN-transformer framework (SRCT) is employed to effectively integrate local bone surface information and long-range relation spinal structure information. Spine reconstruction texture framework (SRTE) is used to extract spine texture features to enhance the effect of pixel-level reconstruction. Experiments show that 3DSRNet achieves excellent 3D spine reconstruction results on multiple metrics including PSNR (45.4666 dB), SSIM (0.8850), CS (0.7662), MAE (23.6696), MSE (9016.1044), and LPIPS (0.0768).