Container-Based Clinical Solutions for Portable and Reproducible Image Analysis

Area of Science:

• Medical Imaging
• Computational Science
• Software Engineering

Background:

• Reproducibility in medical imaging analysis is crucial for reliable scientific findings.
• Current methods for software deployment and environment configuration present challenges in clinical settings.
• Linux containers offer a solution for abstracting, installing, and configuring complex computational environments.

Purpose of the Study:

• To explore the efficacy of Linux containers in medical image analysis within clinical settings.
• To identify shortcomings of current containerization approaches in this domain.
• To demonstrate a practical use-case of Linux containers for medical image analysis.

Main Methods:

• Exploration of related technologies for environment abstraction and configuration.
• Demonstration of a simple use-case for Linux containers in medical image analysis.
• Evaluation of the potential and limitations of containers in clinical environments.

Main Results:

• Linux containers facilitate the distribution of self-contained, repeatable software environments.
• While not yet mainstream, containers show promise for improving reproducibility in clinical medical imaging.
• A demonstrated use-case highlights the practical application and benefits.

Conclusions:

• Linux containers are a valuable tool for enhancing the reproducibility of complex computations in medical image analysis.
• Their adoption in clinical settings is encouraged to improve the reliability of scientific methods and findings.
• Further integration of containerization technologies is recommended for the advancement of medical imaging research and practice.