Merging/Filtering/Voting to Improve Segmentation of Diabetic Retinopathy Eye Fundus Lesions

Abstract

Diabetic Retinopathy (DR) is a fast-progressing disease affecting millions of people world-wide. An early diagnosis is very important to prevent further damage, which can be done by analysis of the Eye Fundus Images (EFI). In this context, deep learning networks can be used to help medical doctors, both by segmenting potential lesions automatically and by classifying the degree of the illness at a certain instant in time. The segmentation task classifies each individual pixel as belonging to either background (BK=non-lesion), microaneurism (MA), soft or hard exudate (SE and HE) or hemorrhage (HM), the optic disk (OD) and the macula (M). Existing deep learning-based segmentation approaches can detect lesions, but there are a relevant number of pixel misclassifications that should be dealt with. Besides calling attention to the issue of correctly evaluating lesions segmentation quality by using the most appropriate metrics, in this paper we investigate the possibility of bringing together the output labelmaps of different deep learning networks and also hardcoded segmentation to improve the end result by means of filtering/merging/voting. Using a publicly available dataset, we show that the approach improves quality significantly as measured using Intersection over the Union IoU (or Jaccard Index JI), from initial IoU scores of 0.9 (BK) 0.09 (MA) 0.17 (HM) 0.29 (HE) 0.18 (SE) 0.8 (OD) to a final score of 0.99 (BK) 0.143 (MA) 0.32 (HM) 0.39 (HE) 0.37 (SE) 0.9 (OD). This corresponds to a significant improvement of around plus 10 percentage points in average. We end the work by delineating future work on this promising direction of research.