Customizing CNNs for blood vessel segmentation from fundus images

For automatic screening of eye diseases, it is very important to segment regions corresponding to the different eye-parts from the fundal images. A challenging task, in this context, is to segment the network of blood vessels. The blood vessel network runs all along the fundal image, varying in density and fineness of structure. Besides, changes in illumination, color and pathology also add to the difficulties in blood vessel segmentation. In this paper, we propose segmentation of blood vessels from fundal images in the deep learning framework, without any pre-processing. A deep convolutional network, consisting of 8 convolutional layers and 3 pooling layers in between, is used to achieve the segmentation. In this work, a Convolutional Neural Network currently in use for semantic image segmentation is customized for blood vessel segmentation by replacing the output layer with a convolutional layer of kernel size 1 × 1 which generates the final segmented image. The output of CNN is a gray scale image and is binarized by thresholding. The proposed method is applied on 2 publicly available databases DRIVE and HRF (capturing diversity in image resolution), consisting of healthy and diseased fundal images boosted by mirror versions of the originals. The method results in an accuracy of 93.94% and yields 0.894 as area under the ROC curve on the test data comprising of randomly selected 23 images from HRF dataset. The promising results illustrate generalizability of the proposed approach.