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Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

J. Paetzold, S. Shit, I. Ezhov, G. Tetteh, A. Ertuerk, B. Menze
clDice-a Novel Connectivity-Preserving Loss Function for Vessel Segmentation
Medical Imaging Meets NeurIPS? 2019 (Workshop), Vancouver, Canada, December 2019. (bib)

Accurate segmentation of vascular structures is an emerging research topic withrelevance to clinical and biological research. The connectedness of the segmentedvessels is often the most significant property for many applications such as dis-ease modeling for neurodegeneration and stroke. We introduce a novel metricnamelyclDice, which is calculated on the intersection of centerlines and volumesas opposed to the traditional dice, which is calculated on volumes only. Firstly,we tested state-of-the-art vessel segmentation networks using the proposed met-ric as evaluation criteria and show that it captures vascular network propertiessuperior to traditional metrics, such as the dice-coefficient. Secondly, we proposea differentiable form ofclDiceas a loss function for vessel segmentation. Wefind that training onclDiceleads to segmentation with more accurate connectivityinformation, higher graph similarity and often superior volumetric scores.
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