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