RAVIR A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis


The vasculature of the retina offers crucial hints that can be used in the diagnosis and ongoing monitoring of systemic diseases such as diabetes and hypertension. The microvascular system plays a primary role in such conditions, and the retina is the only anatomical site where the microvasculature can be directly observed. This is because the retina is the only site where blood vessels are visible. The objective evaluation of retinal vessels has been thought of as a surrogate biomarker for systemic vascular diseases for a long time. As a result of recent developments in retinal imaging and computer vision technologies, this subject has become the focus of renewed attention. In this article, we present a novel dataset for the semantic segmentation of retinal arteries and veins in infrared reflectance (IR) imaging. We have given it the name RAVIR (Retinal Arteries and Veins in Infrared Reflectance). It makes it possible to create models based on Deep Learning that can determine the type of extracted vessel without requiring a significant amount of post-processing. We propose a novel Deep Learning-based methodology, which we will refer to as SegRAVIR, for the semantic segmentation of retinal arteries and veins and the quantitative measurement of the widths of segmented vessels. This methodology will be used for the semantic segmentation of retinal arteries and veins. Our exhaustive experiments not only prove that SegRAVIR is effective, but also show that it has superior performance when compared to other models that are considered to be state-of-the-art. In addition, we propose a knowledge distillation framework as a means of adapting RAVIR pretrained networks to work with color images. On the DRIVE, STARE, and CHASE DB1 datasets, we show that our pretraining procedure is capable of producing new state-of-the-art benchmarks.

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