# BEAC-Net: Boundary-Enhanced Adaptive Context Network for Optic Disk and Optic Cup Segmentation
## 5 Experiments
### Results on Cross-Dataset  
In this part, we use the RIM-ONE-v3 and DRISHTI-GS datasets for testing and introduce the 66 Vision-Tech dataset to test the generalization ability of the BEAC-Net. Figure 11 shows the training results, including the accuracy curve, Dice curve, HD curve, and loss curve, for training the BEAC-Net network on the DRISHTI-GS dataset. As shown in the figure, the curves of the network converge faster in the early training period, indicating that the BEAC-Net network has a strong learning ability, and as the training rounds reach 50, the curves of the network training begin to level off with only a small fluctuation.  
Table 2 shows the comparisons of the quantitative results of the RIM-ONE-v3 and DRISHTI-GS datasets. Our proposed model was compared with U-Net, Deeplabv3+, Encoder-Decoder CE-Net, M-Net, Ensemble CNN, U-shaped convolutional neural network, Robust, and Swin-Unet, which are widely used in the field of semantic segmentation. BEAC-Net outperforms the second-best Swin-Unet, with a Dice value of 0.8582 and IoU value of 0.8385 for OD segmentation on the RIM-ONE-v3 dataset, leading by 0.017 and 0.0284, respectively. In particular, the segmentation results of OC achieve excellent performance, with a Dice value of 0.8087 and IoU value of 0.7633 on the DRISHTI-GS dataset. Through extensive experiments, the validity of BEAC-Net was verified, and it was able to leverage the boundary information to provide supplementary information for better segmentation.  
Figure 8: Results of OD and OC segmentation images on the RIM-ONE-v3 dataset, where green colors indicate OD, and red colors indicate OC.  
Figure 9: Results of OD and OC segmentation images on the DRISHTI-GS dataset, where green colors indicate OD, and red colors indicate OC.  
Figure 11: (**a**) Acc curve, (**b**) Dice score curve, (**c**) Hausdorff Distance curve, (**d**) Loss curve for DRISHTI-GS dataset.  
Figure 10: Results of OD and OC segmentation images on the 66 Vision-Tech dataset, where green colors indicate OD, and red colors indicate OC.  
We test the model's generalization ability across different datasets. In Table 3, on the 66 Vision-Tech dataset, our proposed BEAC-Net outperforms the other models. Compared to the segmentation results with the proposed BEAC network and other methods, the BEAC network performs best in the Dice and IoU metrics, reaching a Dice value of 0.8267 and IoU value of 0.8138 for the OD and a Dice value of 0.8057 and IoU value 0.7858 for the OC. Compared with Swin-Unet, leading by 0.0118 and 0.0086 for the OD and 0.0616 and 0.0043 for the OC, respectively, BEAC-Net is more sensitive to the segmentation boundary. We can see that BEAC-Net has the best HD performance for the segmentation of the OD and the OC, with the HD of the OD reaching 8.63 and the HD of the OC reaching 9.59, indicating that the ACM module implements the process of capturing both local-scale and long-range information to improve the segmentation accuracy of the pixels in the boundary region. The results show the good generalization properties of our proposed BEAC-Net model and demonstrate that it is an effective approach for a better presentation of image details in the segmentation results.