Frangi-Net: A Neural Network Approach to Vessel Segmentation
This is an incremental improvement for medical image analysis, specifically in vessel segmentation for fundus images.
The paper tackled vessel segmentation in fundus images by reformulating the Frangi vesselness measure into a trainable neural network called Frangi-Net, resulting in up to a 17% increase in F1 score after fine-tuning.
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.