arXiv AI
Jun 16
Leptomeningeal Collateral Detection on DSA via Vessel-Graph Neural Networks
arXiv:2606. 14828v1 Announce Type: cross Abstract: Leptomeningeal collaterals (LMCs) are an important prognostic factor in acute ischemic stroke.
Temporally Consistent Graph Extraction and Matching for Longitudinal Angiographic Images
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.
arXiv Machine Learning
Jul 28
Evaluation of Blood Vessel Segmentation Methods on Hard-to-Detect Vascular Structures
arXiv:2406. 13128v2 Announce Type: replace-cross Abstract: Due to the intricate structure of vascular trees, minor segmentation errors can significantly alter connectivity patterns and increase variability in extracted morphological properties.
arXiv Computer Vision
Sep 15
BSC-Net: A Small-Branch-Sensitive Structural Continuity Network for Coronary Vessel Segmentation and Quantitative Angiographic Analysis
arXiv:2609.15400v1 Announce Type: new Abstract: Vessel segmentation in X-ray coronary angiography (XCA) is a fundamental step for quantitative coronary analysis and subsequent assessment of coronary...
Hugging Face Trending Papers
Jun 23
A Dual Edge Spatial Jacobian Image Graph for Interpretable Diabetic Retinopathy Grading
Automated diabetic retinopathy (DR) grading from colour fundus photographs can achieve strong predictive performance, but clinical interpretation requires more than an image-level label. It requires understanding how lesion evidence is distributed around retinal vessels and how this evidence relates to quantitative vascular biomarkers.
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...
arXiv Computer Vision
Sep 11
SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies
SegKAN is a new model for high‑resolution medical image segmentation that tackles fragmentation and noise in hepatic vessel CT scans. It replaces the standard embedding module with a novel convolutional network to smooth noise and avoid gradient explosion, and reinterprets spatial relationships between Patch blocks as temporal relationships to better capture positional dependencies. Experiments on a hepatic vessel dataset show a 1.78% improvement in Dice score over the current state‑of‑the‑art model, indicating that the new structure enhances segmentation performance for extended objects.
arXiv Machine Learning
Jul 28
Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging
arXiv:2607. 23371v1 Announce Type: cross Abstract: Vascular segmentation is a standard procedure for clinical diagnosis, yet the specific visual features determining model decisions remain poorly understood.
arXiv Machine Learning
Sep 17
Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations
The paper introduces a biology-informed heterogeneous graph representation that models retinal vessel segments, intercapillary areas, and the foveal avascular zone to predict diabetic retinopathy stages from OCTA images. This graph-based approach reframes staging as a graph-level classification task solved with a graph neural network, achieving AUC-ROC values up to 84% and outperforming biomarker-based classifiers, CNNs, and vision transformers. The method also provides detailed, interpretable explanations by precisely localizing abnormal vessels and non-perfusion areas.
Hugging Face Trending Papers
Jul 25
Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging
Vascular segmentation is a standard procedure for clinical diagnosis, yet the specific visual features determining model decisions remain poorly understood. This paper investigates the visual cues Convolutional Neural Networks (CNNs) use to segment blood vessels across two distinct imaging domains: fluorescence microscopy and retinal fundus photography.
arXiv Computer Vision
Sep 2
UI-VISA: U-Net Initialized Vascular Image Segmentation Architecture
arXiv:2609.01598v1 Announce Type: new Abstract: Accurate segmentation of vascular structures in digital subtraction angiography (DSA) images remains challenging due to the thin, elongated, and branch...
arXiv Computer Vision
Sep 25
Mind the Gap: Mesh-Guided Repair of Broken Vessels
The paper introduces a mesh-guided post‑processing framework that repairs broken vessel segmentations produced by nnU‑Net. By fitting a deformable template mesh to each predicted binary mask, the method conservatively reconnects disconnected components using thin bridge candidates while respecting foreground‑growth constraints. Evaluation on aorta, Circle of Willis, and pulmonary artery datasets shows that connectivity metrics (ccDice and Betti‑0) improve dramatically with negligible change in overall overlap (Dice).