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

翻译：我们提出了一种基于深度学习的颅骨重建方法，该方法基于 MONAI 框架开发，并在 MUG500+ 颅骨数据集上进行了预训练。该实现遵循 MONAI 贡献指南，因此易于试用、使用和扩展，可供 MONAI 用户使用。本文的主要目标在于研究在 MONAI 框架下将代码和预训练深度学习模型开源化的问题。如今，软件开源化，尤其是深度学习模型（包括预训练模型）的开源化，已变得愈发重要。近年来，医学图像分析领域经历了巨大变革。十多年前，算法需要使用 C 或 C++ 等低级编程语言实现和优化，才能在性能不及当今计算机的台式机上以合理时间运行。而如今，用户可借助 Python 等高级脚本语言，以及 PyTorch 和 TensorFlow 等框架，并手握海量公开代码仓库。因此，过去需要数千行 C 或 C++ 代码的实现，如今仅用几行脚本即可完成，且执行时间大幅缩短。为更上一层楼，医学开放人工智能网络（MONAI）框架将医学影像研究流程设计得更为便捷，这能够推动并促进整个领域的发展。MONAI 框架是一个免费、社区支持、开源的基于 PyTorch 的框架，它使得研究人员能够向他人提供包含预训练模型的研究贡献。颅骨重建的代码和预训练权重已公开于：https://github.com/Project-MONAI/research-contributions/tree/master/SkullRec