Cranial Implant Design via Virtual Craniectomy with Shape Priors

TL;DR

This paper introduces deep learning models for automatic cranial implant reconstruction from CT scans, outperforming baselines and handling out-of-distribution cases effectively.

Contribution

Proposes and evaluates two deep learning approaches, including one with shape priors, for automated cranial implant design from CT images.

Findings

Direct estimation model outperforms baseline methods.

Shape prior model is more robust to out-of-distribution shapes.

Models show promising accuracy in cranial implant reconstruction.

Abstract

Cranial implant design is a challenging task, whose accuracy is crucial in the context of cranioplasty procedures. This task is usually performed manually by experts using computer-assisted design software. In this work, we propose and evaluate alternative automatic deep learning models for cranial implant reconstruction from CT images. The models are trained and evaluated using the database released by the AutoImplant challenge, and compared to a baseline implemented by the organizers. We employ a simulated virtual craniectomy to train our models using complete skulls, and compare two different approaches trained with this procedure. The first one is a direct estimation method based on the UNet architecture. The second method incorporates shape priors to increase the robustness when dealing with out-of-distribution implant shapes. Our direct estimation method outperforms the baselines provided by the organizers, while the model with shape priors shows superior performance when dealing with out-of-distribution cases. Overall, our methods show promising results in the difficult task of cranial implant design.