Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning
This work addresses the problem of improving segmentation reliability for ocular disease assessment in medical imaging, offering an incremental advance by integrating uncertainty quantification into existing methods.
The paper tackled retinal layer segmentation in OCT images by proposing a Bayesian deep learning method that provides pixel-wise uncertainty measures, resulting in comparable or improved performance and enhanced robustness against noise, validated on a dataset of 1487 images from 15 subjects.
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.