# Frangi-Net: A Neural Network Approach to Vessel Segmentation

arXiv: https://arxiv.org/abs/1711.03345 · HTML: https://mlsanity.com/paper/1711.03345/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future](https://mlsanity.com/paper/2108.04543/) (2021)
- [SVS-net: A Novel Semantic Segmentation Network in Optical Coherence Tomography Angiography Images](https://mlsanity.com/paper/2104.07083/) (2021)
- [VessShape: Few-shot 2D blood vessel segmentation by leveraging shape priors from synthetic images](https://mlsanity.com/paper/2510.27646/) (2025)

## Abstract (arXiv)

In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network ("Frangi-Net"), and illustrate that the Frangi-Net is equivalent to the original Frangi filter. Furthermore, we show that, as a neural network, Frangi-Net is trainable. We evaluate the proposed method on a set of 45 high resolution fundus images. After fine-tuning, we observe both qualitative and quantitative improvements in the segmentation quality compared to the original Frangi measure, with an increase up to 17% in F1 score.