# When Experts Disagree: Characterizing Annotator Variability for Vessel Segmentation in DSA Images

arXiv: https://arxiv.org/abs/2508.10797 · HTML: https://mlsanity.com/paper/2508.10797/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 14 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes](https://mlsanity.com/paper/1803.09340/) (2018)
- [Uncertainty-Aware Segmentation Quality Prediction via Deep Learning Bayesian Modeling: Comprehensive Evaluation and Interpretation on Skin Cancer and Liver Segmentation](https://mlsanity.com/paper/2508.01460/) (2025)

## 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.