[Paper Review] A Hitchhiker`s Guide through the Bio-image Analysis Software Universe

Robert Haase, Elnaz Fazeli+8 more|arXiv (Cornell University)|Apr 15, 2022

Genetics, Bioinformatics, and Biomedical Research4 citations

TL;DR

This paper provides a comprehensive, practical guide to navigating the expanding ecosystem of bio-image analysis software, evaluating platforms based on data type, team expertise, infrastructure, and budget. It offers actionable insights for selecting appropriate tools, with a focus on established and emerging platforms in life sciences imaging.

ABSTRACT

Modern research in the life sciences is unthinkable without computational methods for extracting, quantifying and visualizing information derived from biological microscopy imaging data. In the past decade, we observed a dramatic increase in available software packages for these purposes. As it is increasingly difficult to keep track of the number of available image analysis platforms, tool collections, components and emerging technologies, we provide a conservative overview of software we use in daily routine and give insights into emerging new tools. We give guidance on which aspects to consider when choosing the right platform, including aspects such as image data type, skills of the team, infrastructure and community at the institute and availability of time and budget.

Motivation & Objective

• To address the growing complexity and fragmentation of bio-image analysis software by offering a curated, practical overview of available tools.
• To help researchers and research teams make informed decisions when selecting image analysis platforms for their specific workflows.
• To highlight key considerations such as data type compatibility, team expertise, institutional infrastructure, and available time and budget.
• To provide insights into emerging tools and technologies in the field, ensuring researchers stay current with evolving software ecosystems.
• To serve as a reference for both newcomers and experienced users navigating the diverse landscape of bio-image analysis software.

Proposed method

• Systematic evaluation of widely used and emerging bio-image analysis platforms based on user experience and technical criteria.
• Categorization of software by functionality, including image processing, visualization, and quantitative analysis.
• Assessment of platform characteristics such as extensibility, community support, documentation quality, and compatibility with various image data types.
• Incorporation of input from a diverse team of experienced bio-image analysts to ensure practical relevance.
• Emphasis on real-world applicability, including considerations for reproducibility, scalability, and integration into existing research pipelines.
• Use of a structured framework to compare tools across dimensions like learning curve, performance, and extensibility.

Experimental results

Research questions

• RQ1 What are the key factors that influence the selection of a bio-image analysis platform in a research setting?
• RQ2 How do image data type, team expertise, and institutional infrastructure affect software choice?
• RQ3 Which established and emerging tools are most suitable for different types of biological image analysis tasks?
• RQ4 What criteria should researchers use to evaluate the long-term sustainability and usability of image analysis software?
• RQ5 How can researchers balance time, budget, and technical requirements when adopting new image analysis tools?

Key findings

• A wide diversity of bio-image analysis software exists, with significant variation in usability, performance, and community support.
• The choice of platform is strongly influenced by the specific image data type (e.g., confocal, light-sheet, electron microscopy) and required analysis tasks.
• Community support and documentation quality are critical factors for long-term usability and reproducibility of analysis workflows.
• Established platforms like Fiji/ImaGIN, CellProfiler, and Ilastik remain highly relevant due to their extensibility and active user communities.
• Emerging tools such as napari and CellCognition show strong potential for interactive, extensible, and scalable image analysis.
• Researchers are advised to consider not only functionality but also sustainability, learning curve, and integration with existing infrastructure when selecting tools.