Convolutional neural networks for deep feature learning in retinal vessel segmentation

Abstract

Analysis of retinal vessels in fundus images provides a valuable tool for characterizing many retinal and systemic diseases. Accurate automatic segmentation of these vessels is usually required as an essential analysis step. In this work, we propose a new formulation of deep Convolutional Neural Networks that allows simple and accurate segmentation of the retinal vessels. A major modification in this work is to reduce the intra-class variance by formulating the problem as a Three-class problem that differentiates: large vessels, small vessels, and background areas. In addition, different sizes of the convolutional kernels have been studied and it was found that a combination of kernels with different sizes achieve the best sensitivity and specificity. The proposed method was tested using DRIVE dataset and it showed superior performance compared to several other state of the art methods. The segmentation sensitivity, specificity and accuracy were found to be 83.97%, 95.62% and 94.56% respectively.