Recent advances in genetic tools, such as fast-responding calcium indicators (e.g., GCaMP6f) as well as the increasingly accessible high-speed, high-resolution and high throughput longitudinal recording systems allowed the acquisition of faster and more detailed images at mesoscale level for months on large cohorts of animals. This generates large amounts of data that need to be stored, pre-processed and analyzed in a timely manner which can become a limiting factor as experiments become larger and more complex.

Introducing the umIToolbox, or umIT for brevity, a MATLAB toolbox designed to assist researchers in visualizing and analyzing large imaging datasets.

You can find the source code for uMIT at GitHub: umIT.

If you encounter any bugs or have suggestions on how to improve the toolbox, let us know by opening an issue in the GitHub issue tracker.
You can also use the project’s discussion forum to share ideas and questions with the developers and other users of the toolbox.