This module enables you to use machine-learning algorithms for segmenting images using pixel-classification. It uses different feature extractors to classify pixels inside an image based on the training data and the labeling provided by the user. There are a variety of use cases because the functionality itself is "data-agnostic", meaning it can be used basically with every kind of image data. To train and use Intellesis segmentation models, Docker Desktop and a corresponding segmentation container need to be installed and running together with your application.

The module has the following main functionality:

• Any user can intuitively train a machine learning model to perform image segmentation without advanced training by simply labeling what shall be segmented.
• Import of any image format readable by the software, incl. CZI, OME-TIFF, TIFF, JPG, PNG and TXM (special import required).
• Creation of pre-defined image analysis settings (*.czias) using machine learning based segmentation that can be used inside the image analysis.
• Integration of the Intellesis Segmentation processing functionality into the OAD environment.

Application
Example:

XRM (X-Ray Microscopy) image from sandstone showing the main steps when working with the Intellesis Segmentation module.

Application
Example:

Cells image with phase gradient contrast on the Celldiscoverer 7 and segmented using Intellesis Segmentation.