Abstract
Wide-field optical coherence tomography (OCT) imaging can enable monitoring of peripheral changes in the retina, beyond the conventional fields of view used in current clinical OCT imaging systems. However, wide-field scans can present significant challenges for retinal layer segmentation. Deep Convolutional Neural Networks (CNNs) have shown strong performance in medical imaging segmentation but typically require large-scale, high-quality, pixel-level annotated datasets to be effectively developed. To address this challenge, we propose an advanced semi-supervised learning framework that combines the detailed capabilities of convolutional networks with the broader perspective of transformers. This method efficiently leverages labelled and unlabelled data to reduce dependence on extensive, manually annotated datasets. We evaluated the model performance on a dataset of 74 volumetric OCT scans, each performed using a prototype swept-source OCT system following a wide-field scan protocol with a 15x9 mm field of view, comprising 11,750 labelled and 29,016 unlabelled images. Wide-field retinal layer segmentation using the semi-supervised approach show significant improvements (P-value < 0.001) of up to 11% against a UNet baseline model. Comparisons with a clinical spectral-domain-OCT system revealed significant correlations of up to 0.91 (P-value < 0.001) in retinal layer thickness measurements. These findings highlight the effectiveness of semi-supervised learning with cross-teaching between CNNs and transformers for automated OCT layer segmentation.