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Optical coherence tomography (OCT) is commonly used to analyze retinal layersfor assessment of ocular diseases. In this paper, we propose a method forretinal layer segmentation and quantification of uncertainty based on Bayesiandeep learning. Our method not only performs end-to-end segmentation of retinallayers, but also gives the pixel wise uncertainty measure of the segmentationoutput. The generated uncertainty map can be used to identify erroneouslysegmented image regions which is useful in downstream analysis. We havevalidated our method on a dataset of 1487 images obtained from 15 subjects (OCTvolumes) and compared it against the state-of-the-art segmentation algorithmsthat does not take uncertainty into account. The proposed uncertainty basedsegmentation method results in comparable or improved performance, and mostimportantly is more robust against noise.

Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning