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| Journal Article | DKFZ-2023-01287 |
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2023
Elsevier
Amsterdam [u.a.]
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Please use a persistent id in citations: doi:10.1016/j.softx.2023.101432
Abstract: We present a deep learning model based on an autoencoder for the reconstruction of cranial and facialdefects using the Medical Open Network for Artificial Intelligence (MONAI) framework, which has beenpre-trained on the MUG500+ and SkullFix dataset. The implementation follows the MONAI contributionguidelines, hence, it can be easily tried out and used, and extended by MONAI users. The primary goalof this paper lies in the investigation of open-sourcing codes and pre-trained deep learning modelsunder the MONAI framework. The pre-trained models generated in this work deliver reasonable resultson the cranial and facial reconstruction task and provide an ideal starting-point for other researchersinterested in further investigating the topic. We released the codes and the pre-trained model at theofficial MONAI ‘research contributions’ GitHub repository: https://github.com/Project-MONAI/researchcontributions/tree/master/SkullRec. This contribution has two novelties: 1. Pre-training an autoencoderon the MUG500+ and SkullFix dataset for cranial and facial reconstruction using MONAI, and opensourcing the codes and weights for other MONAI users; 2. Demonstrating that existing MONAI tutorialscan be easily adapted to new use cases, such as skull (cranial and facial) reconstruction.
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