When Experts Disagree: Characterizing Annotator Variability for Vessel Segmentation in DSA Images
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Abstract (arXiv)
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.
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References · 4
- 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
- 2020clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentationcited by 294
- 2018DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumescited by 62
- 2025Uncertainty-Aware Segmentation Quality Prediction via Deep Learning Bayesian Modeling: Comprehensive Evaluation and Interpretation on Skin Cancer and Liver Segmentationcited by 2
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