EAR: Edge-Aware Reconstruction of 3-D vertebrae structures from bi-planar X-ray images
This work addresses the need for accurate 3-D spinal reconstruction from X-rays to aid in diagnosis and surgical planning for medical professionals, representing an incremental improvement over existing methods.
The paper tackles the problem of reconstructing 3-D vertebrae structures from 2-D X-ray images, which is challenging due to lost spatial information and difficulty preserving edges and shapes, and the proposed Edge-Aware Reconstruction network achieves superior performance with metrics like 86.44% Dice and 23.7612 PSNR compared to state-of-the-art models.
X-ray images ease the diagnosis and treatment process due to their rapid imaging speed and high resolution. However, due to the projection process of X-ray imaging, much spatial information has been lost. To accurately provide efficient spinal morphological and structural information, reconstructing the 3-D structures of the spine from the 2-D X-ray images is essential. It is challenging for current reconstruction methods to preserve the edge information and local shapes of the asymmetrical vertebrae structures. In this study, we propose a new Edge-Aware Reconstruction network (EAR) to focus on the performance improvement of the edge information and vertebrae shapes. In our network, by using the auto-encoder architecture as the backbone, the edge attention module and frequency enhancement module are proposed to strengthen the perception of the edge reconstruction. Meanwhile, we also combine four loss terms, including reconstruction loss, edge loss, frequency loss and projection loss. The proposed method is evaluated using three publicly accessible datasets and compared with four state-of-the-art models. The proposed method is superior to other methods and achieves 25.32%, 15.32%, 86.44%, 80.13%, 23.7612 and 0.3014 with regard to MSE, MAE, Dice, SSIM, PSNR and frequency distance. Due to the end-to-end and accurate reconstruction process, EAR can provide sufficient 3-D spatial information and precise preoperative surgical planning guidance.