Abstract
Age-related macular degeneration (AMD) is a progressive retinal disease, causing vision loss. A more detailed characterization of its atrophic form became possible thanks to the introduction of Optical Coherence Tomography (OCT). However, manual atrophy quantification in 3D retinal scans is a tedious task and prevents taking full advantage of the accurate retina depiction. In this study we developed a fully automated algorithm segmenting Retinal Pigment Epithelial and Outer Retinal Atrophy (RORA) in dry AMD on macular OCT. 62 SD-OCT scans from eyes with atrophic AMD (57 patients) were collected and split into train and test sets. The training set was used to develop a Convolutional Neural Network (CNN). The performance of the algorithm was established by cross validation and comparison to the test set with ground-truth annotated by two graders. Additionally, the effect of using retinal layer segmentation during training was investigated. The algorithm achieved mean Dice scores of 0.881 and 0.844, sensitivity of 0.850 and 0.915 and precision of 0.928 and 0.799 in comparison with Expert 1 and Expert 2, respectively. Using retinal layer segmentation improved the model performance. The proposed model identified RORA with performance matching human experts. It has a potential to rapidly identify atrophy with high consistency.
Highlights
Age-related macular degeneration (AMD) is a progressive retinal disease, causing vision loss
An Optical Coherence Tomography (OCT)-based classification system allows accounting for those structural details and is no longer restricted to only the typically well-defined geographic atrophy in atrophic AMD seen in fundus autofluorescence (FAF) images
The Convolutional Neural Network (CNN) performed within the accuracy range that can be found between e xperts[2]
Summary
Age-related macular degeneration (AMD) is a progressive retinal disease, causing vision loss. A new classification system for atrophy in AMD has been proposed, based on spectral-domain optical coherence tomography (SD-OCT)2,3—a modality providing 3D depiction of the retina at micrometer resolution. It allows for differentiating the affected retinal layers (outer retinal atrophy (ORA) versus retinal pigment epithelium and outer retinal atrophy (RORA)) as well as for distinguishing the completeness of the atrophic changes and as a result a more detailed pathology characteristics. The aim of our study was to develop a fully-automated algorithm to detect and measure RORA in macular SD-OCT volume scans according to the most recent definition[2,3] To this end, a DL model based on CNN was developed. In contrast to other methods, it relies on choroid hypertransmission, which is insufficient to Scientific Reports | (2021) 11:21893
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