cp_measure: API-first feature extraction for image-based profiling workflows
This tool addresses barriers to automated and reproducible analyses for computational biologists, though it is incremental as it builds on existing CellProfiler capabilities.
The researchers tackled the challenge of automating and reproducing image-based profiling workflows in biological image analysis by developing cp_measure, a Python library that extracts CellProfiler's measurement capabilities into a modular API-first tool, demonstrating high fidelity with original features and enabling scalable, reproducible pipelines for machine learning applications.
Biological image analysis has traditionally focused on measuring specific visual properties of interest for cells or other entities. A complementary paradigm gaining increasing traction is image-based profiling - quantifying many distinct visual features to form comprehensive profiles which may reveal hidden patterns in cellular states, drug responses, and disease mechanisms. While current tools like CellProfiler can generate these feature sets, they pose significant barriers to automated and reproducible analyses, hindering machine learning workflows. Here we introduce cp_measure, a Python library that extracts CellProfiler's core measurement capabilities into a modular, API-first tool designed for programmatic feature extraction. We demonstrate that cp_measure features retain high fidelity with CellProfiler features while enabling seamless integration with the scientific Python ecosystem. Through applications to 3D astrocyte imaging and spatial transcriptomics, we showcase how cp_measure enables reproducible, automated image-based profiling pipelines that scale effectively for machine learning applications in computational biology.