FDAU-Net: An efficient optic disc segmentation network with flexible wavelet convolution block and selective dual-attention fusion gate

Accurate segmentation of optic disc regions in fundus images is essential for the diagnosis and monitoring ocular fundus pathology of ophthalmic diseases such as myopia, glaucoma, and diabetic retinopathy. However, challenges such as irregular disc shapes, edge detail preservation, and limited annotated datasets hinder the effectiveness of existing methods. To handle these problems, we propose FDAU-Net, an efficient, fully convolutional network for optic disc segmentation. FDAU-Net integrates the Flexible Wavelet Convolution Block (FWC Block), which combines the dynamic receptive field adjustment of AKConv with wavelet down-sampling to achieve efficient feature extraction while retaining critical edge information. Additionally, we design the Selective Dual-Attention Fusion Gate (SDA Gate) to optimize feature selection and fusion, leveraging channel and spatial attention mechanisms to substantially enhance segmentation accuracy and robustness. To further improve performance, we introduce MedAugment, an automated data augmentation method tailored for medical images, efficiently boosts data diversity in scenarios with low datasets. Experimental results on the iChallenge and IDRiD datasets demonstrate that FDAU-Net consistently achieves superior segmentation performance across key metrics, highlighting its potential for advancing clinical applications.