Loosely coupled level sets for retinal layer segmentation in optical coherence tomography

This paper presents a novel method for the segmentation of layered structures that have a predefined order. Layers are jointly segmented by simultaneous detection of their interfaces. This is done by means of a level set approach based on Bayesian inference where the ordering of the layers is enforced via a novel level set coupling. The method was applied to in-vivo images of healthy human retinas acquired by optical coherence tomography (OCT). A quantitative comparison with manual annotations was used to estimate the method´s accuracy, which showed very good agreement (mean absolute deviation (MAD) of 3.11-8.58 μm). The large errors were mainly due to differences in handling the vessels. Based on repeated OCT images of the same eye acquired on consecutive days, the reproducibility of manual and automated segmentations, expressed by the MAD of the RNFL thickness, were 10.97 μm and 7.68 μm.