In many ways, creating a pipeline that uses DL Instance or Semantic Segmentation is no different to creating any other pipelines. There is nothing special in the way the objects created by DL segmentation are handled compared to any other pipeline created segments. The Features available are the same, including Custom Features, and they can be used for any downstream segment processing operations, including tracking, parent-child analysis, and segment morphology operations to mention just a few. This ability to do both DL segmentation and traditional segmentation, and use the resulting segments all in the same pipeline, with the ability to batch process, is one of the key strengths of the arivis approach.

To create an analysis pipeline that uses DL we start the same way we always do to create pipelines, that is to say we open the Analysis panel, either from the Analysis menu or from the Shortcuts toolbar.

Then, in the Analysis panel we can create a new pipeline by using + New Pipeline, or choose an existing pipeline to modify. 

With the pipeline open, we can set up the Input ROI and any other operations as needed, and add the Deep Learning Segmenter to the pipeline using + Add Operation.

Note that there are two ways to use DL in pipelines.

The Deep Learning Reconstruction can use a model to create new images of the probability maps from that model. These probability maps can be used like any channel in the pipeline. This includes filtering (denoising, morphology, image math etc), and segmentation. We can, for example, use a Blob Finder on a probability map of a semantic model to obtain an instance segmentation result. Dell Learning Reconstruction only support ONNX or CZANN models.

However, the majority of cases will call for the Deep Learning Segmenter which uses the model to generate objects form the image.

Once we've added the Deep Learning Segmenter to our pipeline, all we need to do is select which model we want to use. If we use ONNX or CZANN file option we then click the browse button and select our model file.

If we use arivis Cloud models, we can either select from previously downloaded models, or open the Model Store to download models as needed.

Once we've selected the model, the operation works like any other segmentation operation. We can preview the results, choose an output tag and colour, and the segmented objects can be used in downstream pipeline operations like any other pipeline objects.