Retinal Vessel Segmentation Using Blending-Based Conditional Generative Adversarial Networks

With a critical need for faster and more accurate diagnosis in medical image analysis, artificial intelligence plays a critical role. Precise artery segmentation and faster diagnosis in retinal blood vessel segmentation can be beneficial for the early detection of acute diseases such as diabetic retinopathy and glaucoma. Recent advancements in deep learning have led to some exciting improvements in the field of medical image segmentation. However, one common problem faced by such methods is the limited availability of labelled data to train a suitable deep learning model. The publicly available dataset for retinal vessel segmentation contains less than 50 images. On the other hand, deep learning is a data-hungry process. We propose a method to generate synthetic images to augment the training needs of the deep learning model. Specifically, we propose a blending and enhancement-based strategy to learn a conditional generative adversarial model. The network synthesizes high-quality fundus images used along with the real images to learn a convolutional neural network-based segmentation model. Experimental evaluation shows that the proposed synthetic generation method improves segmentation performance on the real test images of the vascular extraction (DRIVE) dataset achieving 97.01% segmentation accuracy.