cp_measure: API-first feature extraction for image-based profiling workflows

TL;DR

cp_measure is a Python library that facilitates automated, reproducible, and scalable image-based profiling by providing API-first access to core CellProfiler features, enhancing machine learning workflows in computational biology.

Contribution

It introduces an API-first, modular Python library that replicates CellProfiler's core measurement capabilities for improved automation and integration in biological image analysis.

Findings

High fidelity with CellProfiler features

Enables seamless integration with Python ecosystem

Supports scalable, reproducible profiling pipelines

Abstract

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.