An automated hybrid decoupled convolutional network for laceration segmentation and grading of retinal diseases using optical coherence tomography (OCT) images

Diabetic retinopathy (DR) is a complication of diabetes that damages the retina and can cause blindness if untreated due to high blood sugar levels. To accurately diagnose and grade DR, it is important to identify retinal lacerations or biomarkers. Optical coherence tomography (OCT) imaging is a commonly used tool by ophthalmologists due to its detailed visualisation of retinal lacerations, which aids in the precise treatment of retinal abnormalities. However, the number of scans obtained daily exceeds the ophthalmologist’s capacity to meaningfully analyse them, given the wide range of severe OCT applications and the prevalence of visual disorders. In the past, several research studies have attempted to address this issue using OCT scans. However, none of them have tried to simultaneously perform retinal laceration segmentation and DR grading. To address this problem, we have proposed a new architecture—a cutting-edge decoupled convolutional network consisting of three distinct modules that work together to achieve accurate DR grading based on clinical standards aided by retinal laceration segmentation. Our proposed paper introduces a deep learning framework that leverages dual guidance to improve performance on two related tasks. It was extensively tested using 26,841 multi-vendor scans, four publicly available datasets, and a real-time dataset containing 307 OCT scans from various patients. The results confirmed the effectiveness of our design, with a mean Dice score of 0.88 (4.76% improvement) in retinal laceration segmentation and 98.93% accuracy in DR grading, with an actual positive rate of about 98.46% and a true negative rate of 99.37%.