Exploiting the inter-rater disagreement to improve probabilistic segmentation

Disagreements among the experts while segmenting a certain region can be observed for complex segmentation tasks. Deep learning based solution Probabilistic UNet is one of the possible solutions that can learn from a given set of labels for each individual input image and then can produce multiple segmentations for each. But, this does not incorporate the knowledge about the segmentation distribution explicitly. This research extends the idea by incorporating the distribution of the plausible labels as a loss term. The proposed method could reduce the GED by 47% and 63% for multiple sclerosis and vessel segmentation tasks.