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Danilo Andrade De Jesus, Sander Wooning, Daniël Luttikhuizen, Muhammad Shirazi, Michael Pircher, Caroline Klaver, Johannes R. Vingerling, Stefan Klein, Theo van Walsum, Luisa Sanchez Brea; . Invest. Ophthalmol. Vis. Sci. 2020;61(9):PB00104.
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Drusen deposits that form between the retinal pigment epithelium (RPE) and Bruch's membrane (BM) are a significant risk factor for age-related macular degeneration (AMD). In this work, a novel way of automatically segmenting the RPE-BM complex in OCT images from multiple devices using a convolutional neural network is proposed.
A total of 15744 B-scans (Bioptigen SD-OCT, NC, USA) from 269 early AMD patients and 115 normal subjects, selected from a publicly available dataset, were used in this study (split on subject level in 70% train, 10% validation and 20% test). For each B-scan, the inner limiting membrane (ILM), RPE, and BM have been manually segmented (Fig. 1). A convolutional neural network (U-Net) with generalized Dice loss function was trained to segment the three retinal layers. Data augmentation was used, including geometric and intensity transformations. Postprocessing was applied to the output of the U-Net, removing the small connected components and using a minimal cost path function to ensure connectivity within each layer. To test the segmentation algorithm, OCT data from three other devices (3D-2000 SD-OCT, Topcon Corporation, Japan; Spectralis SD-OCT, Heidelberg Engineering, Germany; MERLIN SS-OCT, Imagine Eyes, France) were used. The segmentation performance was assessed using Dice score and visual inspection by an experienced AMD specialist.
The Dice score for the Bioptigen test set was 0.97. Visual assessment confirmed that the predictions of the model in the B-scans from the four different devices followed the retinal layers accurately. The predicted RPE-BM complex was color coded displayed as a function of the RPE-BM distance (abnormal thickness indicated in red), thus allowing the location and analysis of the drusen deposits (Fig. 2).
The proposed approach is a first step towards the automation of soft drusen quantification and follow up, as it enables the automatic assessment of the RPE-BM complex in healthy and AMD data from four different OCT devices.
This is a 2020 Imaging in the Eye Conference abstract.
B-scan from the Bioptigen test set with the manually segmented retinal layers (ILM, BM, and RPE) overlayed (A); annotated mask (B); prediction with the RPE-BM complex highlighted (C).
Model predictions and highlighted RPE-BM complex on each dataset: Bioptigen (A); Spectralis (B); Topcon (C); MERLIN (D). Different acquisition settings were considered between devices.
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