Masked Registration and Autoencoding of CT Images for Predictive Tibia Reconstruction

• URL: http://arxiv.org/abs/2512.09525v1
• Date: Wed, 10 Dec 2025 11:04:28 GMT
• Title: Masked Registration and Autoencoding of CT Images for Predictive Tibia Reconstruction
• Authors: Hongyou Zhou, Cederic Aßmann, Alaa Bejaoui, Heiko Tzschätzsch, Mark Heyland, Julian Zierke, Niklas Tuttle, Sebastian Hölzl, Timo Auer, David A. Back, Marc Toussaint,
• Abstract summary: We address the challenge of predicting a patient-specific reconstruction target from a CT of a fractured tibia.<n>Our ap- proach combines neural registration and autoencoder models.
• Score: 6.613247712629387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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