Subsampling strategies to improve learning-based retina vessel segmentation

The proper segmentation of the vascular system of the retina has a very important role in automatic screening systems. Its detection helps the localization of other anatomical parts and also the detection of possible vascular disorders. State-of-the-art machine learning algorithms are reported to have good performance in this field. However, with the spatial resolution of the fundus images growing, it is necessary to decrease the number of training pixels to save computations. In this paper, we investigate several subsampling strategies with the motivation to find the best segmentation results with involving fewer pixels into the analyses. Besides checking the computational advantages, we demonstrate how the segmentation accuracy drops with the level of subsampling.