When Experts Disagree: Characterizing Annotator Variability for Vessel Segmentation in DSA Images
This addresses the challenge of inconsistent annotations in medical imaging for clinicians and researchers, but it is incremental as it builds on existing uncertainty quantification methods.
The paper tackled the problem of variability in segmentations of cranial blood vessels in 2D DSA images by analyzing multiple annotators to characterize and quantify segmentation uncertainty, with the result being a framework to guide additional annotations and develop uncertainty-aware automatic segmentation methods.
We analyze the variability among segmentations of cranial blood vessels in 2D DSA performed by multiple annotators in order to characterize and quantify segmentation uncertainty. We use this analysis to quantify segmentation uncertainty and discuss ways it can be used to guide additional annotations and to develop uncertainty-aware automatic segmentation methods.