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

Year: 2025

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.1117/12.3086577

Authors: Geshvadi, M., So, G., Chlorogiannis, D. D., Galvin, C., Torio, E., Azimi, A., Tachie-Baffour, Y., Haouchine, N., Golby, A., Vangel, M., Wells, W. M., Epelboym, Y., Du, R., Durupinar, F., Frisken, S.

Institutions: Brigham and Women's Hospital, University of Massachusetts Boston, University of Waterloo, Harvard University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Institutes of Health

OpenAlex ID: W6979315739

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