% IMPORTANT: The following is UTF-8 encoded. This means that in the presence % of non-ASCII characters, it will not work with BibTeX 0.99 or older. % Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or % “biber”. @ARTICLE{Li:277125, author = {J. Li and A. Ferreira and B. Puladi and V. Alves and M. Kamp and M. Kim and F. Nensa and J. Kleesiek$^*$ and S.-A. Ahmadi and J. Egger}, title = {{O}pen-source skull reconstruction with {MONAI}}, journal = {SoftwareX}, volume = {23}, issn = {2352-7110}, address = {Amsterdam [u.a.]}, publisher = {Elsevier}, reportid = {DKFZ-2023-01287}, pages = {101432}, year = {2023}, 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.}, cin = {ED01}, ddc = {004}, cid = {I:(DE-He78)ED01-20160331}, pnm = {899 - ohne Topic (POF4-899)}, pid = {G:(DE-HGF)POF4-899}, typ = {PUB:(DE-HGF)16}, doi = {10.1016/j.softx.2023.101432}, url = {https://inrepo02.dkfz.de/record/277125}, }