Active learning of the ground truth for retinal image segmentation

Abstract

Diabetic retinopathy and other eye-related diseases can be diagnosed from eye fundus images by medical experts who look for specific lesions in the images. Automated diagnosis methods can help medical doctors to increase the diagnosis accuracy and decrease the time needed. In order to have a proper dataset for training and evaluating the methods, a large set of images should be annotated by several experts to form the ground truth. To enable efficient utilization of expert’s time, active learning is studied to accelerate the collection of the ground truth. Since one of the important steps in the retinal image diagnosis is the blood vessels segmentation, the corresponding approaches were studied. Two approaches were implemented and extended by proposed active learning methods for selecting the next image to be annotated. The performance of the methods in the case of standard implementation and active learning application was compared for several retinal images databases.