Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases. In this paper, we propose a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning. Our method not only performs end-to-end segmentation of retinal layers, but also gives the pixel wise uncertainty measure of the segmentation output. The generated uncertainty map can be used to identify erroneously segmented image regions which is useful in downstream analysis. We have validated our method on a dataset of 1487 images obtained from 15 subjects (OCT volumes) and compared it against the state-of-the-art segmentation algorithms that does not take uncertainty into account. The proposed uncertainty based segmentation method results in comparable or improved performance, and most importantly is more robust against noise.

该论文提出了一种基于贝叶斯深度学习的视网膜层分割和不确定性量化方法，其不仅可以进行视网膜层的端对端分割，而且还可以提供像素级的不确定性度量。通过生成的不确定性地图，可以识别出错误分割的图像区域，从而用于下游分析。该方法在视网膜图像分割方面具有可比或更好的性能，并且更加稳健抗噪声。

基于贝叶斯深度学习的光学相干层析成像视网膜层联合分割和不确定性可视化