Uncertainty-Guided Semi-Automated Editing of CNN-based Retinal Layer Segmentations in Optical Coherence Tomography

Abstract

Convolutional neural networks (CNNs) have enabled dramatic improvements in the accuracy of automated medical image segmentation. Despite this, in many cases, results are still not reliable enough to be trusted ''blindly''. Consequently, a human rater is responsible to check correctness of the final result and needs to be able to correct any segmentation errors that he or she might notice. For a particular use case, segmentation of the retinal pigment epithelium and Bruch's membrane from Optical Coherence Tomography, we develop a system that makes this process more efficient by guiding the rater to segmentations that are most likely to require attention from a human expert, and by developing semi-automated tools for segmentation correction that exploit intermediate representations from the CNN.We demonstrate that our automated ranking of segmentation uncertainty correlates well with a manual assessment of segmentation quality, and with distance to a ground truth segmentation. We also show that, when used together, uncertainty guidance and our semi-automated editing tools decrease the time required for segmentation correction by more than a factor of three.