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arXiv · 1711.03345

Frangi-Net: A Neural Network Approach to Vessel Segmentation

Weilin Fu·Katharina Breininger·Tobias Würfl·Nishant Ravikumar·Roman Schaffert·Andreas Maier
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Abstract

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.

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Weilin Fu, Katharina Breininger, Tobias Würfl, Nishant Ravikumar, Roman Schaffert, Andreas Maier. 2017-11-09. Frangi-Net: A Neural Network Approach to Vessel Segmentation. https://arxiv.org/abs/1711.03345

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