Once trainings have been created, actually using them in a pipeline is just as easy as using any other pipeline operation, and the results of a DL segmentation can be used in a pipeline in exactly the same way as any other pipeline segments. This includes the ability to:

• Apply object modifications like splitting, dilating, or eroding
• Classify segmented objects based on any of the available object features, and filter out objects that don't meet any given criteria
• Establish Parent/Child relationships or measure distances between objects, including with other objects that were not created through DL
• Export object features and segments masks if needed

The simplest way to use a trained network is to create a new pipeline that uses this network immediately after training. At the bottom of the DL Trainer panel, once the training is complete we can click on Open in pipeline. The software will automatically open the analysis panel, create a new pipeline, add the Deep Learning Segmenter operation to the pipeline, and select the trained model for use with the operation.

With our trained model in the pipeline, we can use this segmentation operation and its output like any other pipeline segmentation operation.

See this other article one using Deep Learning in pipelines for more information on configuring such pipelines to DL models, including custom models created elsewhere, including ZEISS arivis Cloud.