# CellProfiler/CellProfiler

An open-source application for biological image analysis

Repository: https://github.com/CellProfiler/CellProfiler
Canonical: https://ross.abutalabs.com/products/cellprofiler
Homepage: http://cellprofiler.org
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-08-31T22:36:57+00:00

## Health v2 (maintenance only)
Score: 68/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 100, release rhythm 8, longevity 100
- inputs: {"age_days": 5629, "days_push": 2, "days_rel": 705, "gap_med": null, "n_releases_24m": 1}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1135, forks 423 (observed 2026-09-01T02:13:54.355371+00:00)

## What it is
CellProfiler is a free, open-source desktop application for quantitative analysis of biological images, letting biologists build modular image-processing pipelines without programming. It can automatically measure phenotypes from thousands to millions of images and export results to spreadsheets or databases.

## Use cases
- measure cell phenotypes from microscopy images
- count and segment cells in fluorescence images
- batch-process thousands of biological images automatically
- build image analysis pipelines without coding
- quantify high-content screening assay images
- export image measurements to a spreadsheet or database
- classify cell phenotypes with machine learning via CellProfiler Analyst

## When to choose
- you are a biologist needing quantitative image measurements without programming
- you need reproducible, modular pipelines for large batches of microscopy images
- you want a mature GUI tool for cell segmentation and feature measurement

## When to avoid
- you need general-purpose computer vision development rather than biological image analysis
- you require a headless library to embed in your own Python code (consider cellprofiler-core or other libraries)
- you need real-time image processing

## Facets
- artifact type: application
- maturity: active
- function: image-processing, computer-vision, machine-learning, gui, data-science
- domain: bioinformatics, image-processing, data-science, healthcare
- platform: windows, python, cross-platform
- tags: biological-imaging, phenotype-analysis, high-content-screening, image-analysis-pipelines, microscopy, macos, linux

## Member repositories
- CellProfiler/CellProfiler (main) score 68

## Provenance
- Observed fields: from GitHub, fetched 2026-09-01T02:13:54.355371+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:36:49.545513+00:00, confidence not recorded.
  - readme: https://github.com/CellProfiler/CellProfiler (fetched 2026-09-01T02:13:54.355371+00:00, sha cae7c9ced3be)
  - homepage: http://cellprofiler.org (fetched 2026-08-29T12:42:07.715970+00:00, sha b590763d56bf)
  - site_page: https://cellprofiler.org/about (fetched 2026-08-29T12:42:07.726903+00:00, sha bb67a0a2eaf1)
  - site_page: https://cellprofiler.org/getting-started (fetched 2026-08-29T12:42:07.724902+00:00, sha 96625b4b0756)
  - registry_pypi: https://pypi.org/pypi/cellprofiler/json (fetched 2026-08-29T12:42:07.731896+00:00, sha faab72d75b61)
  - site_page: https://cellprofiler.org/releases (fetched 2026-08-29T12:42:07.728724+00:00, sha 4268553e6474)
  - site_page: http://cellprofiler.org/releases (fetched 2026-08-29T12:42:07.730294+00:00, sha dab24968caad)
- Data as of 2026-08-30T08:39:29.467469+00:00.