MIU-Net: Advanced multi-scale feature extraction and imbalance mitigation for optic disc segmentation

Abstract

Pathological myopia is a severe eye condition that can cause serious complications like retinal detachment and macular degeneration, posing a threat to vision. Optic disc segmentation helps measure changes in the optic disc and observe the surrounding retina, aiding early detection of pathological myopia. However, these changes make segmentation difficult, resulting in accuracy levels that are not suitable for clinical use. To address this, we propose a new model called MIU-Net, which improves segmentation performance through several innovations. First, we introduce a multi-scale feature extraction (MFE) module to capture features at different scales, helping the model better identify optic disc boundaries in complex images. Second, we design a dual attention module that combines channel and spatial attention to focus on important features and improve feature use. To tackle the imbalance between optic disc and background pixels, we use focal loss to enhance the model’s ability to detect minority optic disc pixels. We also apply data augmentation techniques to increase data diversity and address the lack of training data. Our model was tested on the iChallenge-PM and iChallenge-AMD datasets, showing clear improvements in accuracy and robustness compared to existing methods. The experimental results demonstrate the effectiveness and potential of our model in diagnosing pathological myopia and other medical image processing tasks.