Abstract: The three-dimensional model of the spine plays an important role in treating spinal disorders such as scoliosis. However, traditional methods for spinal three-dimensional reconstruction suffer from issues such as long processing time, subjectivity, and high radiation exposure. We propose a spinal three-dimensional reconstruction network based on biplanar X-ray images, termed X2S-Net. The network takes the anteroposterior and lateral X-ray images of the patient as input and reconstructs the corresponding voxel model of the spine using a parallel encoder, a three-dimensional reconstruction module, and a segmentation supervision module, achieving end-to-end generation from X-ray images to visualized three-dimensional models. In the feature extraction stage, X2S-Net employs a parallel feature encoder designed for the characteristics of biplanar X-ray images to extract spatial information of the spine and incorporates a multi-scale channel attention mechanism for feature extraction. In the three-dimensional modeling stage, X2S-Net combines traditional image segmentation tasks with a segmentation supervision module to improve the three-dimensional reconstruction results. The experimental results on the dataset demonstrate that this method effectively utilizes the input information from biplanar X-ray images for three-dimensional reconstruction of the spine, achieving an average Hausdorff distance of 6.95mm and a Dice coefficient of 92.01% across the datasets.

摘要： 脊柱的三维模型在治疗脊柱侧弯等脊柱疾病时发挥着重要的作用，但传统的脊椎三维重建方法存在耗时长、主观性强、辐射大等问题。为应对这些挑战，本文提出了一种基于双平面X线片的脊柱三维重建网络，即X2S-Net。该网络利用患者的正位和左侧位X线片作为输入，通过双视角平行编码器、三维重建模块以及分割监督模块后重建出对应位置的脊柱体素模型，实现了从X线片到可视化三维模型的端到端生成。X2S-Net在特征提取阶段使用了针对双平面X线片特点而设计的平行特征编码器用于提取脊柱的空间信息，并设计了多尺度通道注意力机制用于提取特征。在三维模型阶段，X2S-Net结合了传统图像分割任务设计了分割监督模块以提高三维重建效果。最终X2S-Net在数据集上的实验结果表明本方法能够充分利用双平面X线片的输入信息对脊柱进行三维重建，各数据集的平均Hausdorff距离达到了6.95mm且Dice系数达到了92.01%。