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The change of retinal vasculature is an early sign of many vascular andsystematic diseases, such as diabetes and hypertension. Different behaviors ofretinal arterioles and venules form an important metric to measure the diseaseseverity. Therefore, an accurate classification of arterioles and venules is ofgreat necessity. In this work, we propose a novel architecture of deepconvolutional neural network for segmenting and classifying arterioles andvenules on retinal fundus images. This network takes the original color fundusimage as inputs and multi-class labels as outputs. We adopt theencoding-decoding structure (Unet) as the backbone network of our proposedmodel. To improve the classification accuracy, we develop a special encodingpath that couples InceptionV4 modules and Cascading Dilated Convolutions (CDCs)on top of the backbone network. The model is thus able to extract and fusehigh-level semantic features from multi-scale receptive fields. The proposedmethod has outperformed the previous state-of-the-art method on DRIVE datasetwith an accuracy of 0.955 $\pm$ 0.002.

Automated segmentaiton and classification of arterioles and venules using Cascading Dilated Convolutional Neural Networks