Automated choroidal segmentation in spectral optical coherence tomography images with geographic atrophy using multimodal complementary information

Abstract. Changes in the choroid have been suggested to be of critical relevance to some eye diseases, e.g., geographic atrophy (GA) of age-related macular degeneration (AMD). Several groups have worked to develop automated choroidal segmentation algorithms in optical coherence tomography (OCT) images. However, the reported algorithms mainly focus on OCT images with early AMD (or eye diseases with mild/moderate retinal damage). GA, an advanced stage of AMD, frequently presents severe retinal damage and those algorithms may be confounded by the zones of increased choroidal reflectivity due to GA damage. The fundus autofluorescence (FAF) signal from GA regions is more distinguishable and detectable than on OCT images. We propose to use a three-dimensional (3-D) graph-based approach with the complementary information from the companion FAF images to handle the 3-D choroidal segmentation difficulty due to GA. We compare the automated approaches with and without using the FAF-derived GA information against the ground truth. With the FAF-derived information, the segmentation performance regarding both choroidal borders is significantly improved (all p  <  0.01). The robustness of our segmentation approach may be of great value for future large-scale quantitative and longitudinal studies of the choroid, particularly in the setting of atrophic AMD.