Surgical planning for complex tibial fractures can be challenging for surgeons, as the 3D structure of the later desirable bone alignment may be diffi- cult to imagine. To assist in such planning, we address the challenge of predicting a patient-specific reconstruction target from a CT of the fractured tibia. Our ap- proach combines neural registration and autoencoder models. Specifically, we first train a modified spatial transformer network (STN) to register a raw CT to a standardized coordinate system of a jointly trained tibia prototype. Subsequently, various autoencoder (AE) architectures are trained to model healthy tibial varia- tions. Both the STN and AE models are further designed to be robust to masked input, allowing us to apply them to fractured CTs and decode to a prediction of the patient-specific healthy bone in standard coordinates. Our contributions include: i) a 3D-adapted STN for global spatial registration, ii) a comparative analysis of AEs for bone CT modeling, and iii) the extension of both to handle masked inputs for predictive generation of healthy bone structures. Project page: https://github.com/HongyouZhou/repair

翻译：复杂胫骨骨折的手术规划对外科医生具有挑战性，因为理想骨对齐的三维结构可能难以想象。为辅助此类规划，我们解决了从骨折胫骨CT图像预测患者特异性重建目标的问题。我们的方法结合了神经配准与自编码器模型。具体而言，首先训练改进的空间变换网络（STN），将原始CT图像配准至联合训练的胫骨原型标准化坐标系。随后，训练多种自编码器（AE）架构以建模健康胫骨的形态变异。STN与AE模型均进一步设计为对掩码输入具有鲁棒性，使其能应用于骨折CT图像，并解码为标准坐标系下患者特异性健康骨骼的预测结果。我们的贡献包括：i) 适用于三维全局空间配准的改进STN，ii) 针对骨骼CT建模的AE对比分析，以及iii) 将两者扩展至处理掩码输入以实现健康骨骼结构的预测性生成。项目页面：https://github.com/HongyouZhou/repair