JuNkIE-CLImax: exploring multidimensional images in notebooks and the terminal

Area of Science:

• Microscopy and Imaging Science
• Computational Biology
• Bioinformatics

Background:

• Modern microscopy techniques produce complex multidimensional image data.
• Visualizing and exploring these images is challenging in text-based computational environments, such as Jupyter notebooks and terminals.
• Existing tools often lack accessibility in these environments, hindering scientific workflows.

Purpose of the Study:

• To develop novel tools for efficient multidimensional image exploration in text-based environments.
• To enhance the accessibility of complex image data analysis within popular scientific computing platforms.
• To facilitate the integration of image exploration into existing data analysis and visualization pipelines.

Main Methods:

• Development of JuNkIE-CLImax, a pair of Python-based multidimensional image explorers.
• Implementation of Rust extensions for performance optimization.
• Design for platform agnosticism and provision of application programming interfaces (APIs).

Main Results:

• JuNkIE-CLImax enables effective multidimensional image exploration in both Jupyter notebooks and terminal environments.
• The tools are written in Python with performance-optimized Rust extensions.
• APIs allow seamless integration into larger image analysis and visualization workflows.

Conclusions:

• JuNkIE-CLImax significantly improves the ability to explore multidimensional microscopy images in text-based environments.
• These tools enhance accessibility and integration for scientific image analysis.
• JuNkIE-CLImax supports complex data analysis pipelines in computational microscopy.