Abstract #0810

Exploiting the inter-rater disagreement to improve probabilistic segmentation

Soumick Chatterjee^1,2,3, Franziska Gaidzik⁴, Alessandro Sciarra^3,5, Hendrik Mattern³, Gabor Janiga⁴, Oliver Speck^3,6,7, Andreas Nürnberger^1,2,7, and Sahani Pathiraja⁸

¹Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany, ²Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg, Magdeburg, Germany, ³Department of Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Magdeburg, Germany, ⁴Laboratory of Fluid Dynamics and Technical Flows, Otto von Guericke University Magdeburg, Magdeburg, Germany, ⁵MedDigit, Department of Neurology, Medical Faculty, University Hospital Magdeburg, Magdeburg, Germany, ⁶German Center for Neurodegenerative Disease, Magdeburg, Germany, ⁷Center for Behavioral Brain Sciences, Magdeburg, Germany, ⁸School of Mathematics & Statistics, University of New South Wales, Sydney, Australia

Synopsis

Keywords: Segmentation, BrainDisagreements 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.

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