# Deep vessel segmentation based on a new combination of vesselness filters

arXiv: https://arxiv.org/abs/2402.14509 · HTML: https://mlsanity.com/paper/2402.14509/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Deep Vessel Segmentation By Learning Graphical Connectivity](https://mlsanity.com/paper/1806.02279/) (2018)
- [Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images](https://mlsanity.com/paper/2003.07999/) (2020)
- [Cascaded multitask U-Net using topological loss for vessel segmentation and centerline extraction](https://mlsanity.com/paper/2307.11603/) (2023)

## Built on by (most cited first, 5 of 5)

- [HarmonySeg: Tubular Structure Segmentation with Deep-Shallow Feature Fusion and Growth-Suppression Balanced Loss](https://mlsanity.com/paper/2504.07827/) (2025)
- [Do Segmentation Models Understand Vascular Structure? A Blob-Based XAI Framework](https://mlsanity.com/paper/2504.11469/) (2025)
- [VessShape: Few-shot 2D blood vessel segmentation by leveraging shape priors from synthetic images](https://mlsanity.com/paper/2510.27646/) (2025)
- [VEELA: A Clinically-Constrained Benchmark for Liver Vessel Segmentation in Computed Tomography Angiography](https://mlsanity.com/paper/2605.22357/) (2026)
- [Do Not Break the Vessels: Structure-Preserving Mean Flow for Vascular Image Translation](https://mlsanity.com/paper/2606.31095/) (2026)

## Abstract (arXiv)

Vascular segmentation represents a crucial clinical task, yet its automation remains challenging. Because of the recent strides in deep learning, vesselness filters, which can significantly aid the learning process, have been overlooked. This study introduces an innovative filter fusion method crafted to amplify the effectiveness of vessel segmentation models. Our investigation seeks to establish the merits of a filter-based learning approach through a comparative analysis. Specifically, we contrast the performance of a U-Net model trained on CT images with an identical U-Net configuration trained on vesselness hyper-volumes using matching parameters. Our findings, based on two vascular datasets, highlight improved segmentations, especially for small vessels, when the model's learning is exposed to vessel-enhanced inputs.