EAR：基于双平面X射线图像的三维椎体结构边缘感知重建
EAR: Edge-Aware Reconstruction of 3-D vertebrae structures from bi-planar X-ray images
AI总结：
针对双平面X射线图像中三维椎体边缘与形状重建难题，提出融合边缘注意力、频率增强及四类损失的EAR网络，在三个公开数据集上优于四种现有模型。
摘要：
X射线图像凭借其快速的成像速度和高分辨率，便利了诊断与治疗过程。然而，由于X射线成像的投影过程，大量空间信息已经丢失。为了准确提供高效的脊柱形态和结构信息，从二维X射线图像重建脊柱的三维结构至关重要。对于当前的重建方法而言，保留非对称椎体结构的边缘信息和局部形状具有挑战性。在本研究中，我们提出了一种新的边缘感知重建网络（Edge-Aware Reconstruction network，EAR），以专注于提升边缘信息和椎体形状方面的性能。在我们的网络中，通过使用自动编码器架构作为主干，提出了边缘注意力模块和频率增强模块，以增强对边缘重建的感知。同时，我们还结合了四个损失项，包括重建损失、边缘损失、频率损失和投影损失。所提出的方法使用三个公开可访问的数据集进行评估，并与四个最先进的模型进行比较。所提出的方法优于其他方法，并在MSE、MAE、Dice、SSIM、PSNR和频率距离方面分别取得了25.32%、15.32%、86.44%、80.13%、23.7612和0.3014的结果。得益于端到端且准确的重建过程，EAR能够提供充足的三维空间信息和精确的术前手术规划指导。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
