A novel convolutional neural network for identification of retinal layers using sliced optical coherence tomography images

Retinal imaging is crucial for observing the retina and accurately diagnosing pathological problems. Optical Coherence Tomography (OCT) has been a transformative breakthrough for developing high-resolution cross-sectional images. It is imperative to delineate the multiple layers of the retina for a proper diagnosis. A novel segmentation-based approach is introduced in this study to identify seven distinct layers of the retina using OCT images. The developed approach presents SliceOCTNet, a customized U-shaped Convolutional Neural Network (CNN) that introduces group normalization and intricate skip connections. Paired alongside a hybrid loss function, the SliceOCTNet outperformed most state-of-the-art approaches. The introduction of Group Normalization in SliceOCTNet stabilized the model and improved layer identification even when working with small datasets. The use of skip connections also contributed to an improvement in the spatial outlook of the model. Implementing a hybrid loss function addresses the class imbalance problem in the dataset. Duke University's spectral-domain optical coherence tomography (SD-OCT) B-scan dataset of healthy and Diabetic Macular Edema (DME) afflicted patients was utilized to train and evaluate the SliceOCTNet. The model accurately recognizes the seven layers of the retina. It can achieve a high dice coefficient value of 0.941 and refine the segmentation process to a higher level of precision.