  In this paper, we introduce a Bayesian deep learning based model for
segmenting the photoreceptor layer in pathological OCT scans. Our architecture
provides accurate segmentations of the photoreceptor layer and produces
pixel-wise epistemic uncertainty maps that highlight potential areas of
pathologies or segmentation errors. We empirically evaluated this approach in
two sets of pathological OCT scans of patients with age-related macular
degeneration, retinal vein oclussion and diabetic macular edema, improving the
performance of the baseline U-Net both in terms of the Dice index and the area
under the precision/recall curve. We also observed that the uncertainty
estimates were inversely correlated with the model performance, underlying its
utility for highlighting areas where manual inspection/correction might be
needed.
