Importance of Data Augmentation and Transfer Learning on Retinal Vessel Segmentation

Automatic segmentation of retinal fundus images for extracting blood vessels is an essential task in the diagnostic classification of hypertension, glaucoma, and diabetic retinopathy, which are the leading causes of blindness. In this paper, we employed a transfer learning strategy for improved retinal vessel extraction. Firstly, we trained the U-NET model on CHASE DB1 and DRIVE databases. By using data augmentations on datasets we enable the U-NET model to learn retinal vessel features better. We examined the data augmentation types, namely, pixel-level transformations and affine transformations. Secondly, we utilized the transfer learning approach on two datasets and achieved comparable results with the state-of-the-art studies on retinal vessel segmentation task. Also, we employed combination of affine and pixel-level transformations to further boost segmentation performance.