DeepNeuro: an open-source deep learning toolbox for neuroimaging.

Area of Science:

• Neuroimaging
• Machine Learning
• Computational Neuroscience

Background:

• Translating deep learning research into clinical neuroimaging practice faces significant implementation challenges.
• Existing deep learning tools often lack seamless integration for neuroimaging data workflows.

Purpose of the Study:

• To introduce DeepNeuro, a Python-based framework designed to streamline the application of deep neural networks in neuroimaging.
• To facilitate the practical implementation of deep learning models for neuroimaging research and clinical translation.

Main Methods:

• Developed DeepNeuro, a Python framework for designing and executing deep learning pipelines.
• Integrated functionalities for data loading, preprocessing, neural network design/training, and result evaluation/visualization.
• Implemented reproducible packaging of neuroimaging preprocessing functions.
• Enabled containerization of pipelines using Docker and Singularity for enhanced shareability.

Main Results:

• DeepNeuro allows users to create end-to-end deep learning pipelines with minimal implementation friction.
• The framework ensures consistent network performance across different users, institutions, and scanners through reproducible packaging.
• Pipelines built with DeepNeuro can be easily shared as Docker or Singularity containers with command-line interfaces.

Conclusions:

• DeepNeuro significantly lowers the barrier to entry for applying deep learning in neuroimaging research.
• The framework promotes reproducibility and facilitates the translation of deep learning models from research to clinical practice.
• DeepNeuro enhances collaboration and accessibility within the neuroimaging community by enabling easy sharing of complex pipelines.