Analyzing Adjacent B-Scans to Localize Sickle Cell Retinopathy In OCTs

Imaging modalities, such as Optical coherence tomography (OCT), are one of the core components of medical image diagnosis. Deep learning-based object detection and segmentation models have proven efficient and reliable in this field. OCT images have been extensively used in deep learning-based applications, such as retinal layer segmentation and retinal disease detection for conditions such as age-related macular degeneration (AMD) and diabetic macular edema (DME). However, sickle-cell retinopathy (SCR) has yet to receive significant research attention in the deep-learning community, despite its detrimental effects. To address this gap, we present a new detection network called the Cross Scan Attention Transformer (CSAT), which is specifically designed to identify minute irregularities such as SCR in cross-sectional images such as OCTs. Our method employs a contrastive learning framework to pre-train OCT images and a transformer-based detection network that takes advantage of the volumetric nature of OCT scans. Our research demonstrates the effectiveness of the proposed network in detecting SCR from OCT images, with superior results compared to popular object detection networks such as Faster-RCNN and Detection Transformer (DETR). Our code can be found in: https://github.com/VimsLab/CSAT .