# A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge

arXiv: https://arxiv.org/abs/2006.12449 · HTML: https://mlsanity.com/paper/2006.12449/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision](https://mlsanity.com/paper/2308.16139/) (2023)
- [A Systematic Collection of Medical Image Datasets for Deep Learning](https://mlsanity.com/paper/2106.12864/) (2021)
- [Deep Learning-based Framework for Automatic Cranial Defect Reconstruction and Implant Modeling](https://mlsanity.com/paper/2204.06310/) (2022)
- [Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation](https://mlsanity.com/paper/2108.05269/) (2021)
- [Deep Generative Networks for Heterogeneous Augmentation of Cranial Defects](https://mlsanity.com/paper/2308.04883/) (2023)
- [Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model](https://mlsanity.com/paper/2204.05703/) (2022)
- [Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder](https://mlsanity.com/paper/2209.14783/) (2022)
- [Improving Deep Learning-based Automatic Cranial Defect Reconstruction by Heavy Data Augmentation: From Image Registration to Latent Diffusion Models](https://mlsanity.com/paper/2406.06372/) (2024)
- [A Semi-automatic Cranial Implant Design Tool Based on Rigid ICP Template Alignment and Voxel Space Reconstruction](https://mlsanity.com/paper/2404.15287/) (2024)
- [Automatic Skull Reconstruction by Deep Learnable Symmetry Enforcement](https://mlsanity.com/paper/2411.17342/) (2024)

## Abstract (arXiv)

In this study, we present a baseline approach for AutoImplant (https://autoimplant.grand-challenge.org/) - the cranial implant design challenge, which, as suggested by the organizers, can be formulated as a volumetric shape learning task. In this task, the defective skull, the complete skull and the cranial implant are represented as binary voxel grids. To accomplish this task, the implant can be either reconstructed directly from the defective skull or obtained by taking the difference between a defective skull and a complete skull. In the latter case, a complete skull has to be reconstructed given a defective skull, which defines a volumetric shape completion problem. Our baseline approach for this task is based on the former formulation, i.e., a deep neural network is trained to predict the implants directly from the defective skulls. The approach generates high-quality implants in two steps: First, an encoder-decoder network learns a coarse representation of the implant from down-sampled, defective skulls; The coarse implant is only used to generate the bounding box of the defected region in the original high-resolution skull. Second, another encoder-decoder network is trained to generate a fine implant from the bounded area. On the test set, the proposed approach achieves an average dice similarity score (DSC) of 0.8555 and Hausdorff distance (HD) of 5.1825 mm. The code is publicly available at https://github.com/Jianningli/autoimplant.