  We present a deep learning-based approach for skull reconstruction for MONAI,
which has been pre-trained on the MUG500+ skull 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. Nowadays, open-sourcing software, especially
(pre-trained) deep learning models, has become increasingly important. Over the
years, medical image analysis experienced a tremendous transformation. Over a
decade ago, algorithms had to be implemented and optimized with low-level
programming languages, like C or C++, to run in a reasonable time on a desktop
PC, which was not as powerful as today's computers. Nowadays, users have
high-level scripting languages like Python, and frameworks like PyTorch and
TensorFlow, along with a sea of public code repositories at hand. As a result,
implementations that had thousands of lines of C or C++ code in the past, can
now be scripted with a few lines and in addition executed in a fraction of the
time. To put this even on a higher level, the Medical Open Network for
Artificial Intelligence (MONAI) framework tailors medical imaging research to
an even more convenient process, which can boost and push the whole field. The
MONAI framework is a freely available, community-supported, open-source and
PyTorch-based framework, that also enables to provide research contributions
with pre-trained models to others. Codes and pre-trained weights for skull
reconstruction are publicly available at:
https://github.com/Project-MONAI/research-contributions/tree/master/SkullRec
