Segmentation Method Selection Dialog

Global Thresholding

Applies a fixed threshold across the entire image to segment objects of interest. Use it for images with consistent object intensity like fluorescence images.

AI Instance Segmentation

Uses a deep learning model trained on arivis Cloud to distinguish and separate individual objects, even if touching or overlapping. Use it for precise identification in complex images. You need the Docker Desktop software running on your PC and have a suitable model available, see Machine Learning and AI Functionalities in ZEN.

AI Semantic Segmentation

Uses a semantic machine learning model trained with Intellesis in ZEN or arivis Cloud for classifying regions. Use it for labeling image regions by predefined classes, using random forest or deep learning. For semantic segmentation models, you need an installation of the 3rd party Python Tools.

Background Subtraction

Uses a rolling ball algorithm to subtract uneven background, then applies a global threshold to segment objects. Use it for images with varying background intensity.

Variance-Based Thresholding

Segments based on intensity changes. Use it for brightfield images where objects are distinguished by variance rather than consistent intensity.

Dynamic Thresholding

Applies local thresholds to different regions to handle inhomogeneous backgrounds. Use it for images with uneven illumination.

Don't show this automatically

Activated: The dialog is not opened automatically anymore for all users and can only be opened with the Explore button.
Deactivated: The dialog opens automatically when entering the respective step of the image analysis.

OK

Uses the selected segmentation method for the class and closes the dialog.