Frangi-Net: A Neural Network Approach to Vessel Segmentation
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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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Nov 2017.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 0; 2025: 1.
3 papers.
- 2021Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Futurecited by 18
- 2021SVS-net: A Novel Semantic Segmentation Network in Optical Coherence Tomography Angiography Imagescited by 0
- 2025VessShape: Few-shot 2D blood vessel segmentation by leveraging shape priors from synthetic imagescited by 0
- 2025VessShape: Few-shot 2D blood vessel segmentation by leveraging shape priors from synthetic images31 Oct 2025
- 2021Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future10 Aug 2021
- 2021SVS-net: A Novel Semantic Segmentation Network in Optical Coherence Tomography Angiography Images14 Apr 2021