A Subpixel Residual U-Net and Feature Fusion Preprocessing for Retinal Vessel Segmentation

Abstract

Retinal Image analysis allows medical professionals to inspect the morphology of the retinal vessels for the diagnosis of vascular diseases. Automated extraction of the vessels is vital for computer-aided diagnostic systems to provide a speedy and precise diagnosis. This paper introduces SpruNet, a Subpixel Convolution based Residual U-Net architecture which re-purposes subpixel convolutions as down-sampling and up-sampling method. The proposed subpixel convolution based down-sampling and up-sampling strategy efficiently minimizes the information loss during the encoding and decoding process which in turn increases the sensitivity of the model without hurting the specificity. A feature fusion technique of combining two types of image enhancement algorithms is also introduced. The model is trained and evaluated on three mainstream public benchmark datasets, and detailed analysis and comparison of the results are provided which shows that the model achieves state-of-the-art results with less complexity. The model can make inference on $512\times 512$ 512 × 512 pixel full image in 0.5 s.