Abstract
Segmentation of lesions in eye fundus images (EFI) is a difficult problem, due to small sizes, varying morphologies, similarities and lack of contrast. Today, deep learning segmentation architectures are state-of-the-art in most segmentation tasks. But metrics need to be interpreted adequately to avoid wrong conclusions, e.g. we show that 90% global accuracy of the Fully Convolutional Network (FCN) does not mean it segments lesions very well. In this work we test and compare deep segmentation networks applied to find lesions in the Eye Fundus Images, focusing on comparison and how metrics really should be interpreted to avoid mistakes and why. In the light of this analysis, we finalize by discussing further challenges that lie ahead.
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