To support ophthalmologists in their daily routine and enable the quantitative assessment of progression of Cytomegalovirus infection as observed on series of retinal angiograms, a methodology allowing an accurate comparison of retinal borders has been developed. In order to evaluate accuracy of borders, ophthalmologists have been asked to repeatedly outline boundaries between infected and noninfected areas. As a matter of fact, accuracy of drawing relies on local features such as contrast, quality of image, background..., all factors which make the boundaries more or less perceptible from one part of an image to another. In order to directly estimate accuracy of retinal border from image analysis, an artificial neural network (a succession of unsupervised and supervised neural networks) has been designed to correlate accuracy of drawing (as calculated form ophthalmologists' hand-outlines) with local features of the underlying image. Our method has been applied to the quantification of CMV retinitis. It is shown that accuracy of border is properly predicted and characterized by a confident envelope that allows, after a registration phase based on fixed landmarks such as vessel forks, to accurately assess the evolution of CMV infection.