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Rapid Analysis and Exploration of Fluorescence Microscopy Images

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Published on: March 19, 2014

Moving beyond the desktop: prospects for practical bioimage analysis via the web.

Wei Ouyang¹, Kevin W Eliceiri², Beth A Cimini³

¹Science for Life Laboratory, Department of Applied Physics, KTH Royal Institute of Technology, Stockholm, Sweden.

Frontiers in Bioinformatics

|August 10, 2023

Summary

Transitioning bioimage analysis to web-based platforms enhances accessibility and collaboration. This shift addresses computational demands and reproducibility challenges in biological imaging research.

Keywords:

bioimage analysis browser computing cloud computing collaboration in science data accessibility and reusability data management web-based tools

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Area of Science:

Life Sciences
Bioimaging
Computational Biology

Background:

Advancements in biological imaging generate complex data, challenging traditional desktop-based analysis methods.
Current bioimage analysis tools face issues with accessibility, installation, and reproducibility.

Purpose of the Study:

To advocate for a transition from desktop to web-based bioimage analysis.
To highlight the benefits of web-based tools for accessibility, collaboration, and workflow efficiency.
To explore the current landscape, challenges, and community involvement in web-based bioimage analysis.

Main Methods:

Perspective-based analysis of current bioimage analysis trends.
Discussion of potential benefits and challenges of web-based platforms.
Exploration of implementation strategies, including prototyping and workflow application.

Main Results:

Web-based bioimage analysis offers reduced local computational needs and overcomes installation barriers.
It provides solutions for enhanced reproducibility and streamlined scientific workflows.
A combined approach of prototyping and large-scale application is suggested for optimal implementation.

Conclusions:

Web-based bioimage analysis democratizes scientific research by improving accessibility and collaboration.
This transition can accelerate biological discovery through efficient and robust platforms.
Collective scientific community involvement is crucial for successful adoption.