Masked Registration and Autoencoding of CT Images for Predictive Tibia Reconstruction

TL;DR

This paper presents a novel neural registration and autoencoding framework that predicts healthy tibia structures from fractured CT scans, aiding surgical planning for complex tibial fractures.

Contribution

It introduces a 3D spatial transformer network for registration, compares autoencoder architectures for tibia modeling, and extends models to handle masked inputs for fracture prediction.

Findings

Effective registration of fractured CTs to a standard coordinate system.

Autoencoder models successfully capture healthy tibia variations.

Models robustly predict healthy bone structures from masked fractured scans.

Abstract

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