Abstract

A significant problem in retinal vessel segmentation (RVS) research is overfitting mainly due to the lack of large datasets. Data augmentation can alleviate this problem. Current augmentation techniques for RVS do not address the main challenges of localized variable intensities and microvessels in retinal images. This thesis proposes a data augmentation technique (termed variable-intensity patches with swirls, VIPs) to create augmented retinal images by randomly adding variable-size and variable-intensity square patches with swirl structures to a training image, online during training. We add patches and swirls without overlapping the retinal vessels. Our variable patches simulate images with illumination changes, and swirls add microvessel-like structures. To evaluate our augmentation technique, we study recent RVS models and examine the impact of their components, including augmentation, augmentation mode, and preprocessing. We then propose both data preprocessing (gamma correction and contrast enhancement) and VIPs augmentation to address challenges in RVS. Our experiments with in- and cross-datasets show that our combined augmentation and preprocessing technique significantly improves the performance of RVS baseline models (e.g., LWNet by 6.68% and SegRNet by 5.29% in AUC measure). Also, our technique is more stable than all related works across RVS models. Our approach helps to reduce the training-validation losses of RVS models and the gap between training and validation losses. We performed ablation studies on our technique: comparing patches versus swirls, looking at the impact of preprocessing, and analyzing its hyperparameters.