Using the built in model for spine detection is clearly very easy and the results are generally very good, however, in some cases better results can be obtained through other segmentation methods. We can then use the Spine Tracer to assign these previously segmented objects to neurites if preferred.

How the spine head segments are segmented isn't important, but they must belong to the current pipeline. This can be done by either segmenting them de novo or by importing the results of previous pipelines. In this example we've segmented the spines using a custom DL model via the Deep Learning Segmenter:

1. Select the Use Segment method to show a couple of new options.
   
2. Select the Trace objects and the Spine segments that we want to link together. The selection is based on the tags created for these objects in the pipeline.
   Note: Only the head of the spines need to be segmented. The neck of the spine is detected automatically from the Trace channel as selected in the operation parameters.
3. Set a maximum spine length to reduce occurrences of incorrectly segmented objects being included as spines. In this case, any object whose center is further than the selected value is not included in the final result.
4. Set a limit for the maximum percentage volume of the spine segment that is allowed to overlap the neurite. Because the neurite and spines are both segmented independently the segments could conceivably overlap and could also include portions of the trace segment.
5. Run the operation.

1. The segments are converted to spines and connected to the neurite: