From cells to pixels: A decision tree for designing bioimage analysis pipelines.
Elnaz Fazeli1, Robert Haase2,3, Michael Doube4
1Biomedicum Imaging Unit, Faculty of Medicine and HiLIFE, University of Helsinki, Helsinki, Finland.
Journal of Microscopy
|August 29, 2025
Summary
This study introduces a framework to help biologists select appropriate image analysis methods for bioimage data. It categorizes structures and links them to specific quantification techniques, improving data interpretation.
Area of Science:
- Life Sciences
- Computational Biology
- Bioimage Analysis
Background:
- Bioimaging advances biological understanding but extracting data is challenging for non-computational experts.
- Complex bioimage datasets require specialized image analysis methods for meaningful information extraction.
Purpose of the Study:
- To provide a general approach for identifying relevant image analysis methods for bioimage datasets.
- To bridge the gap between biologists and computational analysts by fostering a common language.
Main Methods:
- Categorization of common bioimage structures into image analysis domains.
- Development of a framework linking structure types to appropriate quantification methods.
- Inclusion of illustrative examples and a visual flowchart for defining analysis objectives.
Main Results:
- A structured approach to match bioimage data types with suitable analysis techniques.
- Enhanced ability for researchers to navigate and interpret complex bioimage datasets.
- Facilitation of clearer communication between biologists and image analysis specialists.
Conclusions:
- The proposed framework empowers researchers to efficiently select image analysis tools for bioimage data.
- Understanding bioimage structures and analysis domains enhances data interpretation and research outcomes.
- Improved communication between disciplines leads to more effective bioimage data analysis.
