CraNeXt is a deep learning model for skull reconstruction tasks. The input is a binary voxel is a defective skull, and the output is a binary voxel representing a complete skull.
For a detailed demonstration of how to use CraNeXt, please refer to our example.ipynb notebook. This notebook provides step-by-step instructions and code examples for:
- Downloading sample SkullBreak dataset and preparing input data
- Using the CraNeXt model with pretrained weights on SkullBreak
- Running skull reconstruction and performing evaluation
- Visualizing reconstruction result
You can run the notebook directly in Google Colab by clicking the Colab badge above or locally.
Use pip to install the requirements as follows:
!pip install -r requirements.txt
Please refer to our full manuscript in IEEE Access. If you use the model, you can cite it with the following bibtex.
@article {CraNeXt,
author = { Kesornsri, Thathapatt and Asawalertsak, Napasara and Tantisereepatana, Natdanai and Manowongpichate, Pornnapas and Lohwongwatana, Boonrat and Puncreobutr, Chedtha and Achakulvisut, Titipat and Vateekul, Peerapon },
journal = { IEEE Access },
title = { CraNeXt: Automatic Reconstruction of Skull Implants With Skull Categorization Technique },
year = { 2024 },
volume = { 12} ,
pages = { 84907--84922 },
keywords = { Skull;Implants;Image reconstruction;Shape measurement;Three-dimensional displays;Computer architecture;Computational modeling;Skull reconstruction;deep learning;skull categorization;autoimplant;volumetric shape completion },
doi = { 10.1109/ACCESS.2024.3415173 },
url = { https://doi.org/10.1109/access.2024.3415173 }
}