Bioimage analysis tasks-to-tools guide

The goal of this guide is to provide a quick reference for selecting the right open-source tool for a given bioimage analysis task.
It is organized by common tasks, such as manual annotation, segmentation, tracking, colocalization, etc., and for each task, a list of commonly used tools is provided.
For more information, please see the About this project page.

Click one of the tiles below to learn more about the tools suitable for that task

Manually annotating regions of interest:

an important image analysis step, for example to indicate regions of interest (ROI) for further analysis or to provide ground-truth for training machine learning (ML) models

Automated segmentation of regions of interest:

involves breaking up an image into regions of interest (ROIs) based on pixel intensity values, texture, shape, etc. rather than by manually annotating them

Tracking cells and particles:

an essential component of analyzing time-lapse studies, it involves detecting and labeling objects frame-by-frame and then linking the objects between frames

Colocalization analysis:

aims to quantify the degree of overlap between two or more channels in an image, for example representing subcellular fluorescence markers

Getting more help

All of tools discussed here are part of the image.sc community, which hosts varied discussions ranging from beginner questions to in-depth bug troubleshooting. Likewise, all of the tasks are well within scope of image.sc discussions. In addition to posting questions or leading discussions, the effective Search, including Advanced filters, enables easy mining of this rich resource. Finally, the image.sc Announcements board is a great way to keep up with the latest bioimaging tool developments.