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

Alán F Muñoz, Tim Treis, Alexandr A. Kalinin, Shatavisha Dasgupta, Fabian J Theis, Anne E Carpenter, Shantanu Singh

Keywords: bioimaging, image-based profiling, spatial transcriptomics, high-content microscopy, morphological profiling, drug discovery, Image Analysis, Feature Extraction, Biological Imaging, Machine Learning, Image-based Profiling, Morphological Profiling, CellProfiler, Computational Biology, Python Library, 3D Imaging, Spatial Transcriptomics, Automated Workflows, Data Science, High-Throughput Screening, Biological Features, Deep Learning, Interpretable Features, Astrocyte Imaging, Reproducibility

TL;DR: A Python library to extract interpretable morphological features from images and masks

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.

Submission Number: 33