# Scalable Robust Graph and Feature Extraction for Arbitrary Vessel Networks in Large Volumetric Datasets

arXiv: https://arxiv.org/abs/2102.03444 · HTML: https://mlsanity.com/paper/2102.03444/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 51 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model](https://mlsanity.com/paper/2007.05201/) (2020)
- [Extracting 3D Vascular Structures from Microscopy Images using Convolutional Recurrent Networks](https://mlsanity.com/paper/1705.09597/) (2017)

## Built on by (most cited first, 17 of 17)

- [Relationformer: A Unified Framework for Image-to-Graph Generation](https://mlsanity.com/paper/2203.10202/) (2022)
- [The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography](https://mlsanity.com/paper/2312.17670/) (2023)
- [3D Vessel Graph Generation Using Denoising Diffusion](https://mlsanity.com/paper/2407.05842/) (2024)
- [Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)](https://mlsanity.com/paper/2108.13233/) (2021)
- [RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection](https://mlsanity.com/paper/2407.12271/) (2024)
- [Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations](https://mlsanity.com/paper/2502.16697/) (2025)
- [Link Prediction for Flow-Driven Spatial Networks](https://mlsanity.com/paper/2303.14501/) (2023)
- [Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers](https://mlsanity.com/paper/2403.06601/) (2024)
- [Semantically Consistent Discrete Diffusion for 3D Biological Graph Modeling](https://mlsanity.com/paper/2507.04856/) (2025)
- [Parametric shape models for vessels learned from segmentations via differentiable voxelization](https://mlsanity.com/paper/2507.02576/) (2025)
- [Circle of Willis Centerline Graphs: A Dataset and Baseline Algorithm](https://mlsanity.com/paper/2510.13720/) (2025)
- [A Patient-Specific Pulmonary Arterial Tree Digital Twin to Extract Pulmonary Embolism Biomarkers](https://mlsanity.com/paper/2605.28217/) (2026)
- [Hierarchical Random Walker Segmentation for Large Volumetric Biomedical Images](https://mlsanity.com/paper/2103.09564/) (2021)
- [A Streaming Volumetric Image Generation Framework for Development and Evaluation of Out-of-Core Methods](https://mlsanity.com/paper/2112.09809/) (2021)
- [Do Segmentation Models Understand Vascular Structure? A Blob-Based XAI Framework](https://mlsanity.com/paper/2504.11469/) (2025)
- [Vesselpose: Vessel Graph Reconstruction from Learned Voxel-wise Direction Vectors in 3D Vascular Images](https://mlsanity.com/paper/2605.00538/) (2026)
- [Temporally Consistent Graph Extraction and Matching for Longitudinal Angiographic Images](https://mlsanity.com/paper/2609.16889/) (2026)

## Abstract (arXiv)

Recent advances in 3D imaging technologies provide novel insights to researchers and reveal finer and more detail of examined specimen, especially in the biomedical domain, but also impose huge challenges regarding scalability for automated analysis algorithms due to rapidly increasing dataset sizes. In particular, existing research towards automated vessel network analysis does not consider memory requirements of proposed algorithms and often generates a large number of spurious branches for structures consisting of many voxels. Additionally, very often these algorithms have further restrictions such as the limitation to tree topologies or relying on the properties of specific image modalities. We present a scalable pipeline (in terms of computational cost, required main memory and robustness) that extracts an annotated abstract graph representation from the foreground segmentation of vessel networks of arbitrary topology and vessel shape. Only a single, dimensionless, a-priori determinable parameter is required. By careful engineering of individual pipeline stages and a novel iterative refinement scheme we are, for the first time, able to analyze the topology of volumes of roughly 1TB on commodity hardware. An implementation of the presented pipeline is publicly available in version 5.1 of the volume rendering and processing engine Voreen (https://www.uni-muenster.de/Voreen/).