# DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes

arXiv: https://arxiv.org/abs/1803.09340 · HTML: https://mlsanity.com/paper/1803.09340/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2018.

## Built on by (most cited first, 30 of 62)

- [Relationformer: A Unified Framework for Image-to-Graph Generation](https://mlsanity.com/paper/2203.10202/) (2022)
- [Topology-Aware Uncertainty for Image Segmentation](https://mlsanity.com/paper/2306.05671/) (2023)
- [vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation](https://mlsanity.com/paper/2411.17386/) (2024)
- [Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives](https://mlsanity.com/paper/2207.11203/) (2022)
- [VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis](https://mlsanity.com/paper/2307.03592/) (2023)
- [Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation](https://mlsanity.com/paper/2101.09321/) (2021)
- [Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images](https://mlsanity.com/paper/2107.01502/) (2021)
- [PointScatter: Point Set Representation for Tubular Structure Extraction](https://mlsanity.com/paper/2209.05774/) (2022)
- [Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)](https://mlsanity.com/paper/2108.13233/) (2021)
- [Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset](https://mlsanity.com/paper/2306.07089/) (2023)
- [3D Vessel Segmentation with Limited Guidance of 2D Structure-agnostic Vessel Annotations](https://mlsanity.com/paper/2302.03299/) (2023)
- [MambaVesselNet++: A Hybrid CNN-Mamba Architecture for Medical Image Segmentation](https://mlsanity.com/paper/2507.19931/) (2025)
- [An Analysis of the Influence of Transfer Learning When Measuring the Tortuosity of Blood Vessels](https://mlsanity.com/paper/2111.10255/) (2021)
- [Cascaded multitask U-Net using topological loss for vessel segmentation and centerline extraction](https://mlsanity.com/paper/2307.11603/) (2023)
- [Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis](https://mlsanity.com/paper/2308.15088/) (2023)
- [3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images](https://mlsanity.com/paper/2309.08481/) (2023)
- [Neurovascular Segmentation in sOCT with Deep Learning and Synthetic Training Data](https://mlsanity.com/paper/2407.01419/) (2024)
- [Trexplorer Super: Topologically Correct Centerline Tree Tracking of Tubular Objects in CT Volumes](https://mlsanity.com/paper/2507.10881/) (2025)
- [Modeling and hexahedral meshing of cerebral arterial networks from centerlines](https://mlsanity.com/paper/2201.08279/) (2022)
- [PARSE challenge 2022: Pulmonary Arteries Segmentation using Swin U-Net Transformer(Swin UNETR) and U-Net](https://mlsanity.com/paper/2208.09636/) (2022)
- [A new dataset for measuring the performance of blood vessel segmentation methods under distribution shifts](https://mlsanity.com/paper/2301.04517/) (2023)
- [3D Vascular Segmentation Supervised by 2D Annotation of Maximum Intensity Projection](https://mlsanity.com/paper/2402.12128/) (2024)
- [Novel Radiomic Measurements of Tumor- Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers](https://mlsanity.com/paper/2210.02273/) (2022)
- [Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging](https://mlsanity.com/paper/2311.13319/) (2023)
- [Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers](https://mlsanity.com/paper/2403.06601/) (2024)
- [VesselSDF: Distance Field Priors for Vascular Network Reconstruction](https://mlsanity.com/paper/2506.16556/) (2025)
- [RefTr: Recurrent Refinement of Confluent Trajectories for 3D Vascular Tree Centerlines](https://mlsanity.com/paper/2511.20823/) (2025)
- [Self-Supervised Vessel Enhancement Using Flow-Based Consistencies](https://mlsanity.com/paper/2101.05145/) (2021)
- [An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes](https://mlsanity.com/paper/2204.12333/) (2022)
- [MLP-GAN for Brain Vessel Image Segmentation](https://mlsanity.com/paper/2207.08265/) (2022)

## Abstract (arXiv)

We present DeepVesselNet, an architecture tailored to the challenges faced when extracting vessel networks or trees and corresponding features in 3-D angiographic volumes using deep learning. We discuss the problems of low execution speed and high memory requirements associated with full 3-D convolutional networks, high-class imbalance arising from the low percentage of vessel voxels, and unavailability of accurately annotated training data - and offer solutions as the building blocks of DeepVesselNet. First, we formulate 2-D orthogonal cross-hair filters which make use of 3-D context information at a reduced computational burden. Second, we introduce a class balancing cross-entropy loss function with false positive rate correction to handle the high-class imbalance and high false positive rate problems associated with existing loss functions. Finally, we generate synthetic dataset using a computational angiogenesis model capable of generating vascular trees under physiological constraints on local network structure and topology and use these data for transfer learning. DeepVesselNet is optimized for segmenting and analyzing vessels, and we test the performance on a range of angiographic volumes including clinical MRA data of the human brain, as well as X-ray tomographic microscopy scans of the rat brain. Our experiments show that, by replacing 3-D filters with cross-hair filters in our network, we achieve over 23% improvement in speed, lower memory footprint, lower network complexity which prevents overfitting and comparable accuracy (with a Cox-Wilcoxon paired sample significance test p-value of 0.07 when compared to full 3-D filters). Our class balancing metric is crucial for training the network and transfer learning with synthetic data is an efficient, robust, and very generalizable approach leading to a network that excels in a variety of angiography segmentation tasks.