BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows

Area of Science:

• Scientific image analysis
• Bioimage informatics
• Computational biology

Background:

• Advanced microscopy generates complex image data requiring sophisticated analysis.
• Current bioimage analysis methods are often too complex for simple written protocols.
• Reproducibility and standardization are critical challenges in scientific image analysis.

Purpose of the Study:

• To introduce BIAFLOWS, an open-source web tool for reproducible deployment and benchmarking of bioimage analysis workflows.
• To provide a platform for comparing diverse bioimage analysis methods, including deep learning.
• To promote high-quality standards and safeguard research results in bioimage analysis.

Main Methods:

• Development of BIAFLOWS, a web-based platform for workflow management.
• Population of BIAFLOWS with 34 image analysis workflows and 15 microscopy datasets.
• Remote execution and assessment of workflows using visual comparison and standard metrics.
• Benchmarking of seven nuclei segmentation workflows, including deep learning approaches.

Main Results:

• BIAFLOWS successfully enables reproducible deployment and remote benchmarking of bioimage analysis workflows.
• The platform facilitates visual and metric-based performance comparisons of different analysis methods.
• Demonstrated effectiveness through a comparative analysis of nuclei segmentation workflows.

Conclusions:

• BIAFLOWS enhances reproducibility and standardization in bioimage analysis.
• The tool facilitates the benchmarking and sharing of diverse image analysis workflows.
• BIAFLOWS is poised to become a valuable resource for the bioimaging community, encouraging contributions of datasets and workflows.