Translating deep learning research from theory into clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a Python-based deep learning framework that puts deep neural networks for neuroimaging into practical usage with a minimum of friction during implementation. We show how this framework can be used to design deep learning pipelines that can load and preprocess data, design and train various neural network architectures, and evaluate and visualize the results of trained networks on evaluation data. We present a way of reproducibly packaging data pre- and postprocessing functions common in the neuroimaging community, which facilitates consistent performance of networks across variable users, institutions, and scanners. We show how deep learning pipelines created with DeepNeuro can be concisely packaged into shareable Docker and Singularity containers with user-friendly command-line interfaces.

将深度学习研究从理论转化为临床实践具有独特的挑战，特别是在神经影像领域。在本文中，我们介绍了 DeepNeuro，这是一种基于 Python 的深度学习框架，可将用于神经成像的深度神经网络投入实际使用，并在实施过程中将摩擦降至最低。我们展示了该框架如何用于设计深度学习管道，这些管道可以加载和预处理数据、设计和训练各种神经网络架构，以及评估和可视化训练网络在评估数据上的结果。我们提出了一种可重复地打包神经影像界常见的数据预处理和后处理功能的方法，这有助于跨可变用户、机构和扫描仪实现网络的一致性能。