Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model
This addresses a critical problem in cranioplasty for patients with irregular cranial defects, offering a more reliable alternative to existing automated methods, though it is incremental as it adapts a known technique to a specific domain.
The paper tackles the challenge of designing cranial implants for large and complex defects, where CNN-based methods fail in clinical practice, and shows that a statistical shape model (SSM) built on segmentation masks can reconstruct such defects with only minor manual corrections, as validated by neurosurgeons.
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