Evaluation of deep learning-based retinal pigment epithelium segmentation for a widely used optical coherence tomography device.

To develop our proposed technology method to improve retinal pigment epithelium (RPE) detection in optical coherence tomography (OCT) images and compare its efficacy with Topcon's automated segmentation algorithm across multiple retinal diseases and healthy eyes. OCT images from 88 patients with age-related macular degeneration (AMD) were used for our proposed technology model training and validation. For testing with separate images were obtained from patients with AMD (100), diabetic retinopathy (DR; 50), epiretinal membrane (ERM; 50), branch retinal vein occlusion (BRVO; 50), and healthy eyes (50). The proposed technology was used to identify RPE in OCT images using the Pyramid Scene Parsing Network on top of ResNet-50. The accuracy of the proposed technology method in RPE detection was measured using the mean absolute error (MAE) and compared with Topcon's automated segmentation algorithm for each retinal condition. As compared with Topcon's automated segmentation algorithm, the proposed technology showed significantly better MAEs across all conditions: AMD (2.18 vs. 4.79), DR (1.69 vs. 3.17), ERM (1.50 vs. 2.67), BRVO (1.86 vs. 2.98), and healthy eyes (1.59 vs. 2.28). Notably, the proposed technology's superiority was most evident in the AMD group. The proposed technology method outperformed Topcon's automated segmentation algorithm in accurately visualizing RPE in OCT images across all tested conditions, especially in AMD. Our results indicate the proposed technology's potential to elevate the RPE segmentation which can lead to enhancing ophthalmology care by providing more accurate OCT imaging analyses.