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. 1 • Steps for the analysis of confocal images through Python • Guidance on the usage of machine learning for cell segmentation • Troubleshooting of common problems encountered during imaging analysis by Python Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. 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.
Sacks et al. (Wed,) studied this question.