TITLE:
Segmentation of Diabetic Retinopathy Lesions: The Common Fallacy and Evaluation of Real Segmenters
AUTHORS:
Pedro Furtado
KEYWORDS:
Semantic Segmentation, Diabetic Retinopathy, EFI, Deep Convolution Neural Networks
JOURNAL NAME:
Open Journal of Medical Imaging,
Vol.10 No.4,
October
28,
2020
ABSTRACT: In the context of automated analysis of eye fundus images, it is an important common
fallacy that prior works achieve very high scores in segmentation of lesions,
and that fallacy is fueled by some reviews reporting very high scores, and
perhaps some confusion with terms. A simple analysis of the detail of the few
prior works that really do segmentation reveals scores between 7% and 70% in
sensitivity for 1 FPI. That is clearly sub-par with medical doctors trained to
detect signs of Diabetic Retinopathy, since they can distinguish well the
contours of lesions in Eye Fundus Images (EFI). Still, a full segmentation of
lesions could be an important step for both visualization and further automated
analysis using rigorous quantification or areas and numbers of lesions to
better diagnose. I discuss what prior work really does, using evidence-based
analysis, and confront with segmentation networks, comparing on the terms used
by prior work to show that the best performing segmentation network outperforms
those prior works. I also compare architectures to understand how the network
architecture influences the results. I conclude that, with the correct
architecture and tuning, the semantic segmentation network improves up to 20
percentage points over prior work in the real task of segmentation of lesions.
I also conclude that the network architecture and optimizations are important
factors and that there are still important limitations in current work.