There are two main analysis types, which both work across light modes and plate formats:

CFU detecting models

CFU detecting models run on each well and use machine learning models which are trained specifically to identify regions of interest on the plate such as a colony growing, a clearing zone forming, or even things like the size of a plantlet as it grows.

Annotations can be turned on in the user interface to understand exactly what a given model is picking up.

The CFU detecting model looks at each frame of each plate during the timelapse as illustrated below:

Running at full throughput, each imaging unit can image and analyze 15 petri dishes or 10 microtiter plates (up to 96-well) at a time, allowing for very high throughput automatic quantification.

The area of each area identified is one of the most common phenotype metrics, but a variety of other data points are also available including various colorimetric outputs (mean RGB or HSV across the area) and the center coordinate for each area.

Analysis data can be exported in CSV format directly from the analysis export tab.

Color-based well analysis models

The color analysis looks at entire wells at once and outputs colorimetric quantifications such as the mean lightness, hue, saturation or RGB color of the well

This is very useful especially for multi-well plates as an accurate indicator of growth rates, pH changes or other colorimetric or absorbance-based measurements.

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