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

arXiv: https://arxiv.org/abs/2507.01163 · HTML: https://mlsanity.com/paper/2507.01163/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision](https://mlsanity.com/paper/2310.18689/) (2023)
- [Morphological Profiling for Drug Discovery in the Era of Deep Learning](https://mlsanity.com/paper/2312.07899/) (2023)

## Built on by (most cited first, 3 of 3)

- [JUMP-lite: Compact, reproducible benchmarking of cell representations](https://mlsanity.com/paper/2608.07632/) (2026)
- [cubic: CUDA-accelerated 3D Bioimage Computing](https://mlsanity.com/paper/2510.14143/) (2025)
- [Same Encoder, Different Winner: A Paired-View Framework for Cell Painting Encoder Evaluation](https://mlsanity.com/paper/2609.12761/) (2026)

## Abstract (arXiv)

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.