Automatic Cranial Defect Reconstruction with Self-Supervised Deep Deformable Masked Autoencoders

TL;DR

This paper introduces a self-supervised deep learning method using masked autoencoders for cranial defect reconstruction, improving accuracy and efficiency without extensive ground-truth annotations.

Contribution

It presents a novel self-supervised approach that enhances data heterogeneity and model generalizability for cranial defect reconstruction.

Findings

Outperforms state-of-the-art methods on SkullBreak and SkullFix datasets.

Provides real-time cranial defect reconstruction.

Reduces need for costly synthetic ground-truth data.

Abstract

Thousands of people suffer from cranial injuries every year. They require personalized implants that need to be designed and manufactured before the reconstruction surgery. The manual design is expensive and time-consuming leading to searching for algorithms whose goal is to automatize the process. The problem can be formulated as volumetric shape completion and solved by deep neural networks dedicated to supervised image segmentation. However, such an approach requires annotating the ground-truth defects which is costly and time-consuming. Usually, the process is replaced with synthetic defect generation. However, even the synthetic ground-truth generation is time-consuming and limits the data heterogeneity, thus the deep models' generalizability. In our work, we propose an alternative and simple approach to use a self-supervised masked autoencoder to solve the problem. This approach by design increases the heterogeneity of the training set and can be seen as a form of data augmentation. We compare the proposed method with several state-of-the-art deep neural networks and show both the quantitative and qualitative improvement on the SkullBreak and SkullFix datasets. The proposed method can be used to efficiently reconstruct the cranial defects in real time.