论文标题
与Monai的开源头骨重建
Open-Source Skull Reconstruction with MONAI
论文作者
论文摘要
我们为MONAI提供了一种基于深度学习的方法，该方法已在MUG500+ Skull数据集上进行了预训练。该实现遵循MONAI贡献指南，因此可以轻松地尝试和使用，并由Monai用户扩展。本文的主要目标在于调查开源法规和Monai框架下的预培训深度学习模型。如今，开源软件，尤其是（预训练）深度学习模型，已经变得越来越重要。多年来，医学图像分析经历了巨大的转变。十年前，必须使用低级编程语言（例如C或C ++）实现和优化算法，才能在合理的时间内运行台式PC，该台式PC不如今天的计算机强大。如今，用户拥有诸如Python之类的高级脚本语言，以及Pytorch和Tensorflow等框架以及手头的公共代码存储库。结果，过去具有数千行C或C ++代码的实现，现在可以用几行脚本进行脚本，并在一小部分时间内执行。为了将其置于更高的水平，人工智能的医学开放网络（MONAI）框架将医学成像研究定制为更方便的过程，这可以提高和推动整个领域。 MONAI框架是一个免费的，由社区支持的，开源和基于Pytorch的框架，还使其能够为他人提供预培养的模型的研究贡献。用于颅骨重建的代码和预训练的权重，可在以下公开场所获得：https：//github.com/project-monai/research-contributions/tree/master/master/skullrec
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
