Diabetic Retinopathy Analysis Challenge 2022

Diabetic retinopathy is one of the leading causes of blindness and affects approximately 78% people, with a history of diabetes of 15 years or longer [1]. DR often causes gradual changes in vasculature structure and resulting abnormalities. DR is diagnosed by visually inspecting retinal fundus images for the presence of retinal lesions, such as microaneurysms (MAs), intraretinal microvascular abnormalities (IRMAs), nonperfusion areas and neovascularization. The detection of these lesions is critical to the diagnosis of DR. With rising popularity, OCT angiography (OCTA) has the capability of visualizing the retinal and choroidal vasculature at a microvascular level in great detail [2]. Specially, swept-source (SS)-OCTA allows additionally the individual assessment of the choroidal vasculature. There are already some works using SS-OCTA to grade for qualitative features of diabetic retinopathy [3-5]. Further, ultra-wide optical coherence tomography angiography imaging (UW-OCTA) modality showed higher burden of pathology in the retinal periphery that was not captured by typical OCTA [6]. Some works already use UW-OCTA on DR analysis [6, 7]. The traditional diagnosis of DR grading mainly relies on fundus photography and FFA, especially for PDR, which seriously endangers vision health. FA is mainly used to detect the presence or absence of new blood vessels. Fundus photography is difficult to detect early or small neovascular lesions. FA is an invasive fundus imaging that cannot be used in patients with allergies, pregnancy, or poor liver and kidney function. The ultra-wide OCTA can non-invasively detect the changes of DR neovascularization, thus it is an important imaging modality to help ophthalmologist diagnose PDR. However, there are currently no works
capable of automatic DR analysis using UW-OCTA. In the process of DR analysis, the image quality of UW-OCTA needs to be assessed first, and the images with better imaging quality are selected. Then DR analysis is performed, such as lesion segmentation and PDR detection. Thus, it is crucial to build a flexible and robust model to realize automatic image quality assessment, lesion segmentation and PDR detection. In order to promote the application of machine learning and deep learning algorithms in automatic image quality assessment, lesion segmentation and PDR detection using UW-OCTA images, and promote the application of corresponding technologies in clinical diagnosis of DR, we provide a standardized ultra-wide (swept-source)
optical coherence tomography angiography (UW-OCTA) data set for testing the effectiveness of various other algorithms. We believe this dataset is an important milestone in automatic image quality assessment, lesion segmentation and DR grading.

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