# BEAC-Net: Boundary-Enhanced Adaptive Context Network for Optic Disk and Optic Cup Segmentation
## 5 Experiments
### Ablation Study  
To validate the effectiveness of the different components of BEAC-Net, we conducted ablation studies on the 66 Vision-Tech dataset. EBPA, AFF, and ASPP influence and Dice Loss are discussed below; we selected modules that do not contain these as the baseline. Table 4 shows the ablation results. The Dice and IoU of segmentation OD are 0.7523 and 0.7636 when using the baseline, respectively, and adding any of the three modules results in a performance improvement. The OD and OC segmentation accuracy is optimized when all three modules are added. (1) Efficient Boundary Pixel Attention  
In the ablation experiments, we compare the baseline and add the EBPA modules, which can reduce the time and space complexity to \(O\Big{(}N\sqrt{N}\Big{)}\) by successively stacking \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**RIM-ONE-v3 Dataset**} & \multicolumn{4}{c}{**DRISHTI-GS Dataset**} \\ \cline{2-9}
**Method** & \multicolumn{2}{c}{**OD**} & \multicolumn{2}{c}{**OC**} & \multicolumn{2}{c}{**OD**} & \multicolumn{2}{c}{**OC**} \\ \cline{2-9}  & **Dice** & **IoU** & **Dice** & **IoU** & **Dice** & **IoU** & **Dice** & **IoU** \\ \hline U-Net [8] & 0.7351 & 0.8206 & 0.7176 & 0.6633 & 0.7887 & 0.8206 & 0.7376 & 0.7533 \\ Deeplabv3+ [15] & 0.7467 & 0.8344 & 0.7236 & 0.6701 & 0.7861 & 0.8344 & 0.7336 & 0.7001 \\ CE-Net [23] & 0.7632 & 0.8478 & 0.7592 & 0.6732 & 0.7932 & 0.8478 & 0.7492 & 0.7532 \\ M-Met [12] & 0.7696 & 0.8114 & 0.7348 & 0.6900 & 0.8026 & 0.8114 & 0.7648 & 0.7300 \\ Ensemble CNN [10] & 0.8132 & N/A & 0.7240 & N/A & 0.8120 & N/A & 0.7740 & N/A \\ U-shaped [33] & 0.8344 & N/A & 0.7564 & N/A & 0.8361 & N/A & 0.7764 & N/A \\ Robust [6] & 0.8410 & 0.8256 & 0.7129 & 0.6633 & 0.8310 & 0.8256 & 0.7945 & 0.7429 \\ Swin-Unet [35] & 0.8412 & 0.8101 & 0.7332 & 0.6633 & 0.8582 & 0.8101 & 0.7822 & 0.7532 \\ Our & 0.8582 & 0.8385 & 0.7333 & 0.6633 & 0.8614 & 0.8385 & 0.8087 & 0.7633 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of segmentation results on RIM-ONE-v3 and DRISHTI-GS dataset among different methods. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**OD**} & \multicolumn{2}{c}{**OC**} & \multicolumn{2}{c}{**OC**} \\ \cline{2-7}  & **Dice** & **IoU** & **HD** & **Dice** & **IoU** & **HD** \\ \hline U-Net [8] & 0.6948 & 0.7256 & 35.82 & 0.6515 & 0.7534 & 39.60 \\ Deeplab [36] & 0.7231 & 0.7512 & 29.74 & 0.6752 & 0.7655 & 33.52 \\ Deeplabv3+ [15] & 0.7554 & 0.7440 & 21.55 & 0.6836 & 0.7521 & 26.23 \\ M-Met [12] & 0.7675 & 0.7778 & 22.16 & 0.6872 & 0.7678 & 30.25 \\ Trans-Unet [26] & 0.7947 & 0.8065 & 18.75 & 0.7102 & 0.7763 & 21.85 \\ Swin-Unet [35] & 0.8149 & 0.8052 & 12.32 & 0.7441 & 0.7815 & 15.65 \\ Our & 0.8267 & 0.8138 & 8.63 & 0.8057 & 0.7858 & 9.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison Between our model and previous methods on 66 Vision-Tech dataset. the two EBPA modules. EBPA can capture the optic disk and cup segmentation boundary context information in the fundus image from the horizontal and vertical directions and reduce the model parameters to obtain a lightweight model. In Table 4, the segmented OC Dice and IoU are improved by 0.0228 and 0.0345, respectively, compared with the baseline after adding EBPA. We perceive that better boundary segmentation results are obtained. (2) Attentional Feature Fusion  
We only use the simple linear operation concatenation when we fuse the high-level features and low-level features in the baseline. As shown in Table 4, the IoU of segmenting the OC using the concatenation module is 0.7108, and after replacing AFF from the baseline, the IOU reaches 0.7547, the segmentation accuracy improves to 0.0439, and HD decreases from 33.56 to 25.50. (3) ASPP influence  
ASPP is used to obtain multi-scale features with different shifted window sizes, and we compared adding ASPP to the baseline. In Table 4, there is a significant performance after adding ASPP; OD Dice and IoU are improved to 0.8148 and 0.7952, respectively.