  Semantic segmentation is one of the key problems in the field of computer
vision, as it enables computer image understanding. However, most research and
applications of semantic segmentation focus on addressing unique segmentation
problems, where there is only one gold standard segmentation result for every
input image. This may not be true in some problems, e.g., medical applications.
We may have non-unique segmentation annotations as different surgeons may
perform successful surgeries for the same patient in slightly different ways.
To comprehensively learn non-unique segmentation tasks, we propose the
reward-penalty Dice loss (RPDL) function as the optimization objective for deep
convolutional neural networks (DCNN). RPDL is capable of helping DCNN learn
non-unique segmentation by enhancing common regions and penalizing outside
ones. Experimental results show that RPDL improves the performance of DCNN
models by up to 18.4% compared with other loss functions on our collected
surgical dataset.
