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

arXiv: https://arxiv.org/abs/2204.05703 · HTML: https://mlsanity.com/paper/2204.05703/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 12 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact](https://mlsanity.com/paper/2011.08184/) (2020)
- [GBM Volumetry using the 3D Slicer Medical Image Computing Platform](https://mlsanity.com/paper/1303.0964/) (2013)
- [A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge](https://mlsanity.com/paper/2006.12449/) (2020)
- [Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation](https://mlsanity.com/paper/2108.05269/) (2021)
- [Smooth Extrapolation of Unknown Anatomy via Statistical Shape Models](https://mlsanity.com/paper/1909.10153/) (2019)
- [Pelvis Surface Estimation From Partial CT for Computer-Aided Pelvic Osteotomies](https://mlsanity.com/paper/1909.10452/) (2019)

## Built on by (most cited first, 3 of 3)

- [Open-Source Skull Reconstruction with MONAI](https://mlsanity.com/paper/2211.14051/) (2022)
- [Probabilistic 3D Correspondence Prediction from Sparse Unsegmented Images](https://mlsanity.com/paper/2407.01931/) (2024)
- [Automatic Skull Reconstruction by Deep Learnable Symmetry Enforcement](https://mlsanity.com/paper/2411.17342/) (2024)

## Abstract (arXiv)

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