Sometimes, it can be tempting to create segmentation categories for problems that could be solved more easily using post-processing techniques. For example, attempting to train an algorithm to distinguish between "small cells" and "large cells" may not be the best approach if the only difference between the two classes is the size of the cells. While it is possible to train such an algorithm using many annotated cells that cover the size boundary between small and large cells, such challenges can often be resolved more simply by filtering the post-processed segmentation output of a generic algorithm that segments cells of all sizes.
Another example of a case where there is no need to create a category is the segmentation of separate classes for objects that are "in" and "out" of focus. You could instead train a model for all objects that you can recognize and use the mean or maximum pixel intensity within the segmented area to determine if an object is sufficiently in focus for your application.