U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans

JOSÉ IGNACIO ORLANDO; PHILLIP SEEBÖCK; HRVOJE BOGUNOVIć; SOPHIE KLIMSCHA; CHRISTOPH GRECHENIG; SEBASTIAN WALDSTEIN; BIANCA S. GERENDAS; URSULA SCHMIDT-ERFURTH

Venecia

Simposio; IEEE International Symposium on Biomedical Imaging (ISBI 2019); 2019

IEEE Engineering in Medicine and Biology Society (EMBS)

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.