Protocol for automated analysis of biological images using Python code
Image analysis in Python provides greater flexibility and an open-source alternative to commercially available software. In this protocol, we present steps for quantifying features in various common biological images in a manner that is tailored to scientists with little coding background. Using example experiments, we outline steps to read in a variety of image file types and prepare them for quantitative analysis. We then discuss approaches for segmenting images, extracting measurements from segmented images, and exporting data. For complete details on the use and execution of this protocol, please refer to Eubanks et al.<sup>1</sup>.
Authors
- Elyse Guadagno
- Benjamin N. Sacks (ORCID: https://orcid.org/0000-0003-0143-6589)
- Ananya Guntur
- Elena Eubanks
- Robert Kimelman
- Jiya Mody
- Jose Cevallos
- Nitya Ravinutala
- Neeharika Rao Suvvari
- Alessandro Crimi
- Alejandro Mosera
- Yiming Huang
- Aryan Doshi
- Sunghoo Jung
- Eleanna Kara
Institutions
- Rutgers, The State University of New Jersey (US)
- Jagiellonian University (PL)
- Johnson University (US)
- Environmental and Occupational Health Sciences Institute (US)
- AGH University of Krakow (PL)
Publication Details
- Journal
- STAR Protocols
- Published
- 2026-02-25
- DOI
- https://doi.org/10.1016/j.xpro.2026.104368
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health