arXiv Computer Vision By Linus Kreitner, Laurin Lux, Carmen Baumann, Daniel Rueckert, Martin J. Menten

Temporally Consistent Graph Extraction and Matching for Longitudinal Angiographic Images

Read the original on arXiv Computer Vision →

The paper introduces a method for extracting and matching vessel graphs from longitudinal angiographic images that improves temporal consistency. By performing an early matching step before refining the graphs—removing spurious bulges and merging junctions using joint information—the approach yields a higher matched area and reduces graph fragmentation compared to traditional separate or no refinement strategies. Experiments on complex retinal vessel graphs confirm these benefits.

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arXiv AI
6d ago

ReG-SAM: Reference Graph-Driven SAM for 2D Foundational Vessel Segmentation

ReG-SAM is a SAM-based framework designed for 2D vessel segmentation in medical images. It introduces reference graph prompt embeddings (GPEs) and vascular prototype embeddings (VPEs) to capture global spatial and fine-grained modality-specific vessel features, respectively. By building a modality-wise vascular database and learning these embeddings from reference masks, ReG-SAM consistently outperforms existing baselines across 19 datasets, especially on thin vessels.

By Donghang Lyu, Zichen Zhang, Oleh Dzyubachyk, Marius Staring
arXiv Computer Vision
Sep 1

retinalysis-vascx: An explainable software toolbox for the extraction of retinal vascular biomarkers

arXiv:2602.08580v4 Announce Type: replace-cross Abstract: Automatic extraction of retinal vascular biomarkers from color fundus images (CFI) is crucial for large-scale studies of the retinal vasculat...

By Jose D. Vargas Quiros, Michael J. Beyeler, Sofia Ortin Vela, EyeNED Reading Center, Sven Bergmann, Caroline C. W. Klaver, Bart Liefers, VascX Research Consortium