## 3 Dataset  
Retinal vessel segmentation, a critical task in diagnosing retinal diseases, demands robust datasets for algorithm development and evaluation. Among the available datasets, DRIVE [46], CHASEDB1 [47], and STARE [48] are notable for their extensive annotations and suitability for retinal vessel segmentation tasks. In our study, we have also incorporated three additional datasets, HRF [49], IOSTAR [11], and LES-AV [5] to ensure a comprehensive evaluation.  
The DRIVE dataset consists of 40 high-resolution fundus images, with seven exhibiting abnormal retinal conditions. These images, captured using a fundus camera with a 45-degree field of view, provide diverse characteristics for training and testing segmentation algorithms. Similarly, the CHASEDB1 dataset, comprises 28 fundus images, with annotation for vessel segmentation tasks. These images, captured using various imaging devices, offer valuable insights into retinal pathology and vessel morphology.  
The STARE (Structured Analysis of the Retina) dataset comprises 20 eye fundus images with a resolution of 700 \(\times\) 605 pixels. It includes a comprehensive collection of data, such as diagnosis codes for each image, expert annotations of visible manifestations, and detailed feature mappings. The dataset contains artery/vein labeling by two experts. The expert labeling makes the STARE dataset a valuable tool for retinal image analysis and machine learning applications.  
The HRF (High-Resolution Fundus) dataset is tailored for retinal vessel segmentation, consisting of 45 images divided into 15 subsets. Each subset includes three types of images: one from a healthy eye, one from an individual with diabetic retinopathy, and one from a glaucoma patient. The images are high-resolution, measuring 3304 \(\times\) 2336 pixels. The dataset is organized with 22 images for training and 23 for testing, providing a diverse set of images to assess segmentation models across various retinal conditions.  
The LES-AV dataset consists of 22 fundus images, each annotated manually for blood vessels, with separate labels for arteries and veins. Meanwhile, the IOSTAR vessel segmentation dataset comprises 30 high-resolution images, each with a resolution of 1024 \(\times\) 1024 pixels. The dataset features expert annotations for retinal vessels, the optic disc, and the artery/vein ratio, making it a comprehensive resource for studying and evaluating vessel segmentation techniques in retinal image analysis.  
For our experiments, we meticulously divided the images into training and testing splits. Specifically, we allocated 20 images from the DRIVE dataset for training and reserved the remaining 20 for testing. In the case of the CHASEDB1 dataset, we allocated 14 images for training and used the remaining 14 for testing. To enhance the robustness of our models, we applied data augmentation techniques exclusively during the training phase, as shown in Figure 1. This approach ensures that our models learn from augmented variations of the training data, therefore improving their generalization capabilities. So, after data augmentation, 160 training images and 40 validation images were used for the DRIVE datasetand 112 training images and 28 validation images for the CHASEDB1 dataset. By adhering to these carefully designed training and testing splits, we aim to conduct comprehensive evaluations while maintaining consistency and rigor in our experimentation process.