# Temporally Consistent Graph Extraction and Matching for Longitudinal Angiographic Images

arXiv: https://arxiv.org/abs/2609.16889 · HTML: https://mlsanity.com/paper/2609.16889/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 15 Sep 2026.

## Builds on (most cited first, 9 of 9)

- [Relationformer: A Unified Framework for Image-to-Graph Generation](https://mlsanity.com/paper/2203.10202/) (2022)
- [Deep Vessel Segmentation By Learning Graphical Connectivity](https://mlsanity.com/paper/1806.02279/) (2018)
- [Scalable Robust Graph and Feature Extraction for Arbitrary Vessel Networks in Large Volumetric Datasets](https://mlsanity.com/paper/2102.03444/) (2021)
- [Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations](https://mlsanity.com/paper/2306.10941/) (2023)
- [Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)](https://mlsanity.com/paper/2108.13233/) (2021)
- [ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset](https://mlsanity.com/paper/2408.11142/) (2024)
- [PVBM: A Python Vasculature Biomarker Toolbox Based On Retinal Blood Vessel Segmentation](https://mlsanity.com/paper/2208.00392/) (2022)
- [Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers](https://mlsanity.com/paper/2403.06601/) (2024)
- [Elastic Registration of Geodesic Vascular Graphs](https://mlsanity.com/paper/1809.05499/) (2018)

## Abstract (arXiv)

Recent advances in angiographic imaging have enabled longitudinal visualization of the microvasculature. Image processing pipelines based on vessel graphs are able to resolve subtle temporal changes at the level of individual blood vessels. However, current strategies for graph extraction, refinement, and matching are highly sensitive, with even minuscule differences in the underlying segmentation map resulting in substantially different vessel graphs. These artifacts severely inhibit the ability to accurately match sequential vessel graphs of the same subject over time. To address this problem, we propose a strategy that matches graphs before jointly refining them. Specifically, we perform an early matching after basic graph extraction before removing spurious bulges and merging junctions in both graphs using joint information. In experiments with complex retinal vessel graphs, we demonstrate that this strategy results in a higher matched area without graph fragmentation compared to separate or no refinement, respectively.