Communication Dans Un Congrès Année : 2026

VesselVerse: A Dataset and Collaborative Framework for Vessel Annotation

Daniele Falcetta
Jon Cleary
Loïc Legris
Massimiliano Domenico Rizzaro
Ioannis Pitsiorlas
Hava Chaptoukaev
Bjoern Menze
Maria A Zuluaga

Résumé

This paper is not about a novel method. Instead, it introduces VesselVerse, a large-scale annotation dataset and collaborative framework for brain vessel annotation. It addresses the critical challenge of data annotation availability in supervised learning segmentation and provides a valuable resource for the community. VesselVerse represents the largest public release of brain vessel annotations to date, comprising 950 annotated images from three public datasets across multiple neurovascular imaging modalities. Its design allows for multi-expert annotations per image, accounting for variations across diverse annotation protocols. Furthermore, the framework facilitates the inclusion of new annotations and refinements to existing ones, making the dataset dynamic. To enhance annotation reliability, VesselVerse integrates tools for consensus generation and version control mechanisms, enabling the reversion of errors introduced during annotation refinement. We demonstrate VesselVerse's usability by assessing inter-rater agreement among four expert evaluators.

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hal-05305421 , version 1 (09-10-2025)

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Daniele Falcetta, Vincenzo Marciano, Kaiyuan Yang, Jon Cleary, Loïc Legris, et al.. VesselVerse: A Dataset and Collaborative Framework for Vessel Annotation. MICCAI 2025, 28th International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, Sep 2025, Daejon, South Korea. pp.655-665, ⟨10.1007/978-3-032-05169-1_63⟩. ⟨hal-05305421⟩
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