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Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

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

Alán F Muñoz, Tim Treis, Alexandr A Kalinin

|September 29, 2025

Summary

This summary is machine-generated.

cp_measure is a new Python library for biological image analysis. It enables automated, reproducible image-based profiling and machine learning workflows by extracting CellProfiler measurements programmatically.

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Area of Science:

Computational Biology
Bioimage Analysis
Machine Learning

Background:

Traditional biological image analysis focuses on specific visual properties.
Image-based profiling quantifies numerous features for comprehensive cellular analysis.
Existing tools like CellProfiler present challenges for automated and reproducible machine learning.

Purpose of the Study:

Introduce cp_measure, a Python library for programmatic feature extraction.
Enable modular, API-first access to CellProfiler's measurement capabilities.
Facilitate automated and reproducible image-based profiling pipelines.

Main Methods:

Developed cp_measure as a Python library.
Extracted CellProfiler's core measurement functions.
Validated feature fidelity and integration with the scientific Python ecosystem.

Main Results:

cp_measure features show high fidelity with CellProfiler.
Seamless integration with the scientific Python ecosystem achieved.
Demonstrated successful applications in 3D astrocyte imaging and spatial transcriptomics.

Conclusions:

cp_measure overcomes barriers in automated and reproducible image-based profiling.
Enables scalable machine learning applications in computational biology.
Facilitates advanced analysis of cellular states, drug responses, and disease mechanisms.