Application program interface for automatic segmentation of retinal layers and fluids in Optical Coherence Tomography - Neovascular Age related Macular degeneration retinal images using deep learning models

Optical coherence tomography is a non-invasive imaging technique that provides micrometer-resolution images of retinal structures. These images can assist in identifying changes under the retina's surface, such as edema. This study proposes a novel deep learning model AR U-Net++ for segmenting retinal layers and fluids. The four retinal layers ILM (Internal Limiting Membrane), IPL (Inner Plexiform Layer), RPE (Retinal Pigment Epithelium), BM (Bruch Membrane), and IRF (Intra Retinal Fluid), SRF (Sub Retinal Fluid), and PED (Pigment Epithelial Detachment) are segmented using AR U-Net++. The proposed architecture AR U-Net++ achieves better accuracy (99.67%), mean IoU (0.84), and dice coefficient (0.94) than the existing models of U-Net, AR U-Net, and AR W-Net. The novelty of the suggested model AR U-Net++ is to identify the exact location and depth of the retinal fluid in between the retinal layers and generating reports that aids the clinicians in the diagnosis of Age related Macular Degeneration.