Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital (AöR), Girardetstraße 2, 45131 Essen, Germany; Computer Algorithms for Medicine Laboratory (Cafe), Graz, Austria; Institute of Computer Graphics and Vision (ICG), Graz University of Technology, Inffeldgasse 16c, 8010 Graz, Austria; Corresponding author at: Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital (AöR), Girardetstraße 2, 45131 Essen, Germany.
André Ferreira
Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital (AöR), Girardetstraße 2, 45131 Essen, Germany; Computer Algorithms for Medicine Laboratory (Cafe), Graz, Austria; ALGORITMI Research Centre/LASI, University of Minho, Braga, Portugal
Behrus Puladi
Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany; Institute of Medical Informatics, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany
Victor Alves
ALGORITMI Research Centre/LASI, University of Minho, Braga, Portugal
Michael Kamp
Cancer Research Center Cologne Essen (CCCE), University Medicine Essen (AöR), Hufelandstraße 55, 45147 Essen, Germany; Institute for Neuroinformatics, Ruhr University Bochum, Germany; Data Science and AI Department, Monash University, Melbourne, Australia
Moon Kim
Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital (AöR), Girardetstraße 2, 45131 Essen, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen (AöR), Hufelandstraße 55, 45147 Essen, Germany; Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany
Felix Nensa
Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital (AöR), Girardetstraße 2, 45131 Essen, Germany; Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany
Jens Kleesiek
Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital (AöR), Girardetstraße 2, 45131 Essen, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen (AöR), Hufelandstraße 55, 45147 Essen, Germany; German Cancer Consortium (DKTK), Partner Site Essen, Hufelandstraße 55, 45147 Essen, Germany
Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital (AöR), Girardetstraße 2, 45131 Essen, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen (AöR), Hufelandstraße 55, 45147 Essen, Germany; Computer Algorithms for Medicine Laboratory (Cafe), Graz, Austria; Institute of Computer Graphics and Vision (ICG), Graz University of Technology, Inffeldgasse 16c, 8010 Graz, Austria; Corresponding author at: Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital (AöR), Girardetstraße 2, 45131 Essen, Germany.
We present a deep learning model based on an autoencoder for the reconstruction of cranial and facial defects using the Medical Open Network for Artificial Intelligence (MONAI) framework, which has been pre-trained on the MUG500+ and SkullFix dataset. The implementation follows the MONAI contribution guidelines, hence, it can be easily tried out and used, and extended by MONAI users. The primary goal of this paper lies in the investigation of open-sourcing codes and pre-trained deep learning models under the MONAI framework. The pre-trained models generated in this work deliver reasonable results on the cranial and facial reconstruction task and provide an ideal starting-point for other researchers interested in further investigating the topic. We released the codes and the pre-trained model at the official MONAI ‘research contributions’ GitHub repository: https://github.com/Project-MONAI/research-contributions/tree/master/SkullRec. This contribution has two novelties: 1. Pre-training an autoencoder on the MUG500+ and SkullFix dataset for cranial and facial reconstruction using MONAI, and open-sourcing the codes and weights for other MONAI users; 2. Demonstrating that existing MONAI tutorials can be easily adapted to new use cases, such as skull (cranial and facial) reconstruction.
