Purpose

The purpose of this study was to develop and evaluate a deep learning network for three-dimensional reconstruction of the spine from biplanar radiographs.

Methods

The proposed approach focused on extracting similar features and multiscale features of bone tissue in biplanar radiographs. Bone tissue features were reconstructed for feature representation across dimensions to generate three-dimensional volumes. The number of feature mappings was gradually reduced in the reconstruction to transform the high-dimensional features into the three-dimensional image domain. We produced and made eight public datasets to train and test the proposed network. Two evaluation metrics were proposed and combined with four classical evaluation metrics to measure the performance of the method.

Results

In comparative experiments, the reconstruction results of this method achieved a Hausdorff distance of 1.85 mm, a surface overlap of 0.2 mm, a volume overlap of 0.9664, and an offset distance of only 0.21 mm from the vertebral body centroid. The results of this study indicate that the proposed method is reliable.

中文翻译：

---

使用卷积神经网络从双平面放射线照片进行三维脊柱重建

目的

本研究的目的是开发和评估深度学习网络，用于根据双平面射线照片对脊柱进行三维重建。

方法

所提出的方法侧重于提取双平面射线照片中骨组织的相似特征和多尺度特征。重建骨组织特征以用于跨维度的特征表示以生成三维体积。在重建过程中逐渐减少特征映射的数量，将高维特征变换到三维图像域。我们制作并制作了八个公共数据集来训练和测试所提出的网络。提出了两种评估指标，并与四种经典评估指标相结合来衡量该方法的性能。

结果

对比实验中，该方法的重建结果实现了Hausdorff距离为1.85 mm，表面重叠为0.2 mm，体积重叠为0.9664，距椎体质心的偏移距离仅为0.21 mm。本研究结果表明所提出的方法是可靠的。