  Age-related macular degeneration (AMD) is one of the leading causes of
permanent vision loss in people aged over 60 years. Accurate segmentation of
biomarkers such as drusen that points to the early stages of AMD is crucial in
preventing further vision impairment. However, segmenting drusen is extremely
challenging due to their varied sizes and appearances, low contrast and noise
resemblance. Most existing literature, therefore, have focused on size
estimation of drusen using classification, leaving the challenge of accurate
segmentation less tackled. Additionally, obtaining the pixel-wise annotations
is extremely costly and such labels can often be noisy, suffering from
inter-observer and intra-observer variability. Quantification of uncertainty
associated with segmentation tasks offers principled measures to inspect the
segmentation output. Realizing its utility in identifying erroneous
segmentation and the potential applications in clinical decision making, here
we develop a U-Net based drusen segmentation model and quantify the
segmentation uncertainty. We investigate epistemic and aleatoric uncertainty
capturing model confidence and data uncertainty respectively. We present
segmentation results and show how uncertainty can help formulate robust
evaluation strategies. We visually inspect the pixel-wise uncertainty and
segmentation results on test images. We finally analyze the correlation between
segmentation uncertainty and accuracy. Our results demonstrate the utility of
leveraging uncertainties in developing and explaining segmentation models for
medical image analysis.
