Automated quality assessment of ultra-widefield angiography images
Abstract:
Systems and methods are provided for fully automated quality assessment of ultra-widefield angiography images. A series of ultra-widefield angiography images of a retina of a patient are obtained and each of the series of ultra-widefield angiography images are provided to a neural network trained on a set of labeled images to generate a quality parameter for each of the series of ultra-widefield angiography images representing a quality of the image. Each of the set of labeled images are assigned to one of a plurality of classes representing image quality. A user interface provides an instruction to a user to obtain a new series of ultra-widefield angiography images of the retina of the patient if no image of the series of ultra-widefield angiography images has a quality parameter that meets a threshold value.
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