Agentic AI Enables Automated CellProfiler Pipeline Design via Multimodal In-Context Learning

October 27, 2025

Designing image analysis pipelines in CellProfiler typically demands expert knowledge and extensive manual tuning, creating barriers to scalability and accessibility in high-throughput biological research. In this proof-of-concept study, we introduce an agentic workflow that harnesses in-context learning and multimodal AI models to automatically generate CellProfiler pipelines from minimal user input.

Our approach enables a vision-language model to function as an autonomous agent: interpreting example images and textual prompts, reasoning over visual features, and producing complete pipeline configurations tailored to new datasets. Leveraging a few-shot learning paradigm, the system generalizes from a single annotated example—comprising an image, text, and documentation context—to generate parameterized pipelines for tasks such as segmentation and object detection, adapted to the biological context presented.

Quantitative evaluations demonstrate that the AI-generated pipelines achieve performance comparable to expert-designed configurations while reducing complexity. These findings highlight the potential of agentic AI to streamline and democratize biomedical image analysis by minimizing domain-specific expertise requirements and simplifying pipeline design.