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Segmentation Learn more about segmentation. Segmentation is an image processing method that identifies and separates different regions of interest.

Improve your segmentation Improving your model is an iterative process. Here you can learn the best practices to improve your segmentation.

Semantic segmentation models Semantic segmentation models trained on ZEISS arivis Cloud can be imported into ZEN, ZEN core, and arivis Pro given that the following version requirements are met.

Importing semantic segmentation models To import your semantic segmentation models trained on arivis Cloud to ZEN, ZEN core, or arivis Pro, you need to: Download your model to your computer. Here's how you can download your model.

Instance segmentation models Instance segmentation models trained on ZEISS arivis Cloud can be directly accessed from ZEN, ZEN core or ZEISS arivis Pro via the AI Model Store.

Accessing instance segmentation models from ZEN, ZEN core or arivis Pro The following step-by-step guide was created using ZEN as an example. ZEN core and arivis Pro provide similar functionality.

Cellpose-Based Segmenter This operation detects automatically cells using pretrained cellpose models.

Cellpose-based Segmenter parameters Parameter Description Channel Sets the channel where the cells will be segmented. Optional channel Sets a second optional channel (e.g., nuclei channel) to support the segmentation.

Intensity Threshold Segmenter parameters By default, the Method: Auto is selected. Depending on the selection, different parameters are available. Parameter Description Channel Sets the processing and analysis target channel.

Intensity Threshold Segmenter parameters By default, the Method: Auto is selected. Depending on the selection, different parameters are available. Parameter Description Channel Sets the processing and analysis target channel.

Intensity Threshold Segmenter parameters By default, the Method: Range is selected. Depending on the selection, different parameters are available. Parameter Description Channel Sets the processing and analysis target channel.

Membrane-based Segmenter parameters Parameter Description Channels Sets the processing and analysis target channels. Min intensity (membrane) Sets an approximation of a membrane with the lowest intensity.

Membrane-based Segmenter parameters Parameter Description Channels Sets the processing and analysis target channels. Min intensity (membrane) Sets an approximation of a membrane with the lowest intensity.

Membrane-based Segmenter results If you have set the parameters correctly, the preview (see Previewing the results ) should look like this:

Membrane-based Segmenter results If you have set the parameters correctly, the preview (see Previewing the results ) should look like this:

Machine Learning Segmenter Automatic objects detection algorithm based on machine learning approach. The ML training (Nuclei and Membrane) was previously created. In most cases, the default settings are suitable, and you don't need to change them.

Machine Learning Segmenter parameters Parameter Description Training Shows the name of the Training that is loaded and some basic information below that. Channels Sets the channels as input to this filter.

Segments Storage To change the Segments storage, select from the available options in the droplist box directly in the Results Storage operation. The options are: Source Image Set This is the original unedited image data.

Blob Finder (Segmentation) The Blob Finder is a popular segmentation operation that is available in arivis.

Tracking (Using segmented objects) The tracking operation is a typical case of an operation that uses existing objects to extract additional information.