Hi @ptrblck, thanks for your explanation above. I am also working on training an image segmentation model with 5 classes where a part of 1-2 classes overlap with each other. See this image for example: 
Here the left is the original Grayscale image and right is the segmentation map. The green portion remains the same across different structures, but at some places, the green segment might be overlapped a bit with the blue one. For example, see the middle bottom segmentation. In terms of annotation, the green annotation is done for an area first and then the blue annotation is done on top of that i.e. the pixels at that location belong to two classes.
So, how do I represent the dataset and train the model for this scenario ? Any suggestions ? I am able to stack the segmentation belonging to different classes across channels at the input but how to deal with the overlap during training ? Will the model be able to learn that overlap ?
Thanks