OCTSeg-UNeXt: an ultralight hybrid Conv-MLP network for retinal pathology segmentation in point-of-care OCT imaging

To enable efficient and accurate retinal lesion segmentation on resource-constrained point-of-care Optical Coherence Tomography (OCT) systems, we propose OCTSeg-UNeXt, an ultralight hybrid Convolution-Multilayer Perceptron (Conv-MLP) network optimized for OCT image analysis. Built upon the UNeXt architecture, our model integrates a Depthwise-Augmented Scale Context (DASC) module for adaptive multi-scale feature aggregation, and a Group Fusion Bridge (GFB) to enhance information interaction between the encoder and decoder. Additionally, we employ a deep supervision strategy during training to improve structural learning and accelerate convergence. We evaluated our model using three publicly available OCT datasets. The results of the comparative experiments and ablation experiments show that our method achieves powerful performance in multiple key indicators. Importantly, our method achieves this high performance with only 0.187 million parameters (Params) and 0.053 G Floating-Point Operations Per second (FLOPs), which is significantly lower than UNeXt (0.246M, 0.086G) and UNet (17M, 30.8G). These findings demonstrate the proposed method's strong potential for deployment in Point-of-Care Imaging (POCI) systems, where computational efficiency and model compactness are crucial.