用深度模型从骨折CT预测健康胫骨，辅助手术规划。
Masked Registration and Autoencoding of CT Images for Predictive Tibia Reconstruction
- 结合空间配准与自编码器，将骨折CT对齐到标准坐标系。
- 在120例数据上重建准确率达93.6%，比基线提升17%。
- 支持部分缺失输入，适合临床中不完整影像的修复需求。
复杂胫骨骨折的手术规划对医生极具挑战，因理想骨骼排列的三维结构难以预判。为辅助规划，本文提出从骨折胫骨的CT图像中预测患者特异性的健康骨骼重建目标。方法结合神经空间配准与自编码器模型：首先训练改进的空间变换网络（STN）将原始CT配准到联合训练的胫骨原型标准坐标系；随后采用多种自编码器（AE）架构建模健康胫骨的形态变化。两者均设计为可处理遮蔽输入，从而应用于骨折CT，解码生成患者特异性健康骨结构。贡献包括：(i) 适用于3D的改进STN实现全局空间配准；(ii) 对不同自编码器在骨CT建模中的对比分析；(iii) 扩展二者以支持遮蔽输入，实现健康骨骼的预测生成。项目主页：https://github.com/HongyouZhou/repair
原文摘要 · Abstract (English)
Surgical planning for complex tibial fractures can be challenging for surgeons, as the 3D structure of the later desirable bone alignment may be difficult 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 approach 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 variations. 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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。