Abstract
Accurate segmentation of retinal blood vessels from retinal images is crucial for detecting and diagnosing a wide range of ophthalmic diseases. Our retinal blood vessel segmentation algorithm enhances microfine vessel extraction, improves edge texture clarity, and normalizes vessel distribution. It stabilizes neural network training for complex retinal vascular features. Channel-aware self-attention (CAS) improves microfine vessel segmentation sensitivity. Heterogeneous adaptive pooling (HAP) facilitates accurate vessel edge segmentation through multi-scale feature extraction. The ghost fully convolutional Rectified Linear Unit (GFCReLU) module in the output convolutional layer captures deep semantic information for better vessel localization. Optimization training with Sparrow-Integrated Lion Optimization Algorithm (SLOA) employs sparrow stochastic updating and annealing to fine-tune parameters. The results of the experiments on our homemade dataset and three public datasets are as follows: Mean Intersection over Union (MIoU) of 80.61%, 76.14%, 76.90%, 74.11%; Dice coefficients of 78.97%, 72.51%, 72.84%, 68.93%; and accuracies of 94.83%, 95.74%, 96.67%, 95.81% respectively. The model effectively segments retinal blood vessels, offering potential for diagnosing ophthalmic diseases. Our dataset is available at https://github.com/ZhouGuoXiong/Retinal-blood-vessels-for-segmentation .