Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model

TL;DR

This paper introduces a statistical shape model for reconstructing large, complex cranial defects, demonstrating its effectiveness over CNN-based methods in clinical scenarios with complex irregularities.

Contribution

A novel SSM built on skull segmentation masks that improves reconstruction of complex cranial defects over existing CNN approaches.

Findings

SSM performs well on complex defects with minor manual corrections.

CNN-based methods struggle with irregular and large defects.

The approach is validated by neurosurgeons for implant quality.

Abstract

Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on complex and irregular cranial defects remains unsatisfactory. In this paper, a statistical shape model (SSM) built directly on the segmentation masks of the skulls is presented. We evaluate the SSM on several cranial implant design tasks, and the results show that, while the SSM performs suboptimally on synthetic defects compared to CNN-based approaches, it is capable of reconstructing large and complex defects with only minor manual corrections. The quality of the resulting implants is examined and assured by experienced neurosurgeons. In contrast, CNN-based approaches, even with massive data augmentation, fail or produce less-than-satisfactory implants for these cases. Codes are publicly available at https://github.com/Jianningli/ssm