Author:
Pedro Furtado
Affiliation:
CISUC, Universidade de Coimbra, Polo II, Coimbra, Portugal
Keyword(s):
Medical Imaging, Deep Learning, Segmentation, EFI.
Abstract:
Analysis of Eye Fundus Images (EFI) allows early diagnosis and grading of Diabetic Retinopathy (DR), detecting micro-aneurisms, exudates, haemorrhages, neo-vascularizations and other signs. Automated detection of individual lesions helps visualizing, characterizing and determining degree of DR. Today modified deep convolution neural networks (DCNNs) are state-of-the-art in most segmentation tasks. But the task of segmenting lesions in EFI is challenging due to sizes, varying shapes, similarity and lack of contrast with other parts of the EFI, so that the results are ambiguous. In this paper we test two DCNNs to do a preliminary evaluation of the strengths and limitations using publicly available data. We already conclude that the accuracies are good but the segmentations still have relevant deficiencies. Based on this, we identify the need for further assessment and suggest future work to improve segmentation approaches.