Computing 3D bone models using traditional Computed Tomography (CT) requires a high-radiation dose, cost and time. We present a fully automated, domain-agnostic method for estimating the 3D structure of a bone from a pair of 2D X-ray images. Our triplet loss-trained neural network extracts a 128-dimensional embedding of the 2D X-ray images. A classifier then finds the most closely matching 3D bone shape from a predefined set of shapes. Our predictions have an average root mean square (RMS) distance of 1.08 mm between the predicted and true shapes, making our approach more accurate than the average achieved by eight other examined 3D bone reconstruction approaches. Each embedding extracted from a 2D bone image is optimized to uniquely identify the 3D bone CT from which the 2D image originated and can serve as a kind of fingerprint of each bone; possible applications include faster, image content-based bone database searches for forensic purposes.

使用传统计算机断层扫描 (CT) 计算 3D 骨骼模型需要高辐射剂量、成本和时间。我们提出了一种完全自动化、与领域无关的方法，用于根据一对 2D X 射线图像估计骨骼的 3D 结构。我们经过三元组损失训练的神经网络提取 2D X 射线图像的 128 维嵌入。然后，分类器从一组预定义的形状中找到最匹配的 3D 骨骼形状。我们的预测预测形状与真实形状之间的平均均方根 (RMS) 距离为 1.08 毫米，这使得我们的方法比其他八种经过检查的 3D 骨骼重建方法所达到的平均值更加准确。从 2D 骨骼图像中提取的每个嵌入都经过优化，可以唯一地识别 2D 图像所源自的 3D 骨骼 CT，并可以作为每块骨骼的一种指纹；可能的应用包括更快的、基于图像内容的骨骼数据库搜索，用于法医目的。