# Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images
## 4 Experiment Implementation  
### Experiment Setup  
In this study, we evaluated the generalization performance of the proposed model using five publicly available fundus image datasets with diverse characteristics and conditions. These datasets differ in terms of imaging equipment, resolution, patient populations, and image quality, thereby indirectly reflecting a variety of clinical conditions. Each dataset possesses unique features related to imaging environments, resolution, annotation methods, and the presence or absence of glaucoma. Therefore, independent experiments were conducted without mixing datasets, focusing on dataset-specific analysis. When a predefined train/test split was available, the training set was further divided into train and validation sets in an 8:2 ratio. When no predefined split was provided, the entire dataset was partitioned into train, validation, and test sets with a 7:2:1 ratio for the experiments. Detailed information is summarized in Table 1.  
The DRIONS-DB (Digital Retinal Images for Optic Nerve Segmentation Database) is a fundus image database designed to evaluate the performance of optic disc segmentation algorithms. It consists of 110 fundus images collected from 55 patients randomly selected at the Ophthalmology Service of Miguel Servet Hospital in Zaragoza, Spain. The images were captured using a color analog fundus camera at a resolution of \(600\times 400\) pixels. Each image was annotated by two medical experts, with OD boundaries marked using 36 landmarks per expert. The Drishti-GS dataset, collected from an Indian patient population, consists of 101 retinal images divided into 50 for training and 51 for testing. Each image is provided in PNG format with a resolution of \(2896\times 1944\) pixels and includes both normal and glaucomatous eyes. The training set contains manual segmentation labels for the OD and OC regions based on annotations from multiple experts. REFUGE (Retina Fundus Glaucoma Challenge) is a dataset provided through the Retina Fundus Glaucoma Challenge held by the MICCAI in Spain. It consists of 1200 fundus images collected from multiple centers split into training (400 images), validation (400 images), and testing (400 images) sets. The images have resolutions of \(2124\times 2056\) and \(1634\times 1634\) pixels and include both normal and glaucomatous cases, reflecting diverse imaging conditions and quality variations. ORIGA (an online retina fundus image database for glaucoma analysis and research) was collected by  
\begin{table}
\begin{tabular}{c c c c} \hline
**Database** & **Healthy (Normal)** & **Glaucoma (Abnormal)** & **Resolution** \\ \hline DRIONS-DB & 84 & 26 & \(600\times 400\) \\ Drishti-GS & 70 & 31 & \(2896\times 1944\) \\ REFUGE & 1440 & 160 & \(2124\times 2056\) (train), \(1634\times 1634\) (test) \\ ORIGA & 482 & 165 & \(3072\times 2048\) \\ G1020 & 625 & 296 & \(3004\times 2423\) \\ \hline \end{tabular}
\end{table}
Table 1: Datasets used in this work.  
the Singapore Eye Research Institute and contains a total of 650 fundus images, including 168 images from glaucomatous patients. It provides accurate boundary labels for the OD and OC as well as CDR information. The images generally exhibit low brightness and a higher level of noise. The G1020 dataset is a large-scale, high-resolution fundus image dataset provided in JPG format with a resolution of 3004 \(\times\) 2423 pixels. It includes precise annotation data for the locations of the OD and OC as well as the size of the neuroretinal rim. The dataset features high image quality with well-preserved fine structural details.  
All experiments were conducted in an Ubuntu 22.04 environment using an Intel 14 th-generation i9 CPU and an Nvidia 4090 GPU. PyTorch 2.4.1 was used as the deep learning framework. The batch size was set to 16. Depending on the experimental conditions, the learning rate was varied between 0.01 and 0.0001, which was key to achieving efficient optimization and faster convergence.