Detecting Double Layer Signs with OCT volumes using a 3D Convolutional Neural Network (CNN)
- Author
- Yuka Kihara, Yingying Shi, Cancan Lyu, Jin Yang, Liang Wang, Xiaoshuang Jiang, Mengxi Shen, Rita Laiginhas, Hironobu Fujiyoshi, Giovanni Gregori, Philip J Rosenfeld, Aaron Y Lee
- Publication
- vestigative Ophthalmology & Visual Science, Vol.62, 2102, 2021
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Purpose : The presence of a double layer sign (DLS) on structural OCT B-scans is a critical predictor for subclinical choroidal neovascularization (CNV), a stage of non-exudative type 1 macular neovascularization (MNV) before onset of exudation. We sought to develop a 3D CNN to detect DLS of any size using only structural OCT B-scans.
Methods : Eyes with a DLS and eyes with drusen (Dr), to serve as a control, were imaged using 6x6mm swept source OCT angiography (SS-OCTA, PLEX Elite 9000, Carl Zeiss Meditec, Dublin, CA). Each scan pattern consisted of 500 A-scans per B-scan with each B-scan repeated twice at each of 500 B-scan positions along the 6mm y-axis. The OCTA data was used for manual labeling of DLS and Dr; only the structural OCT was used for deep learning.
Results : A total of 232 eyes (196 patients; 173 with DLS and 53 with Dr) were imaged using the SS-OCTA scan pattern. The deep learning model for multi-region segmentation (Figure 1) labels DLS and Dr on a single B-scan image (3D-2D model). We generated dense annotations by integrating manual annotations and predicted segmentation (Figure 2). After refining the labels, we trained a final 3D convolutional model that segments volumetrically (3D-3D model). Finally, eyes with MNV were identified based on en-face projection maps of the predicted masks. Accuracy of final classification was 92.85% (3D-2D model) and 94.28% (3D-3D model). Mean intersection over union (IoU) was DLS: 31.39%, Dr: 12.23% for the 3D-2D model, and DLS: 57.36%, Dr: 25.20% for the 3D-3D model. Conclusions : Our network can detect DLS from structural B-scans alone by applying an annotation refinement technique for 3D CNN to a dataset with coarse annotations.