## 5 Conclusions  
In this paper, we developed a hybrid CNN-transformer and polar transformation network for OD and OC segmentation. We handled this segmentation task from a fundamentally new perspective, where the OD and OC were represented and segmented in the polar coordinate space rather than in the original image space. Specifically, we combined the CNN network and transformer to explore the polar representation of fundus images with the transformer to exploit contextualized features among all input units to greatly improve each pixel's representational discrimination ability. The polar origin detector is of great importance for OD and OC segmentation. In addition, a polar converter was introduced to train the neural network and use the polar coordinate transformation of the original fundus datasets, which balances the view cup view disc ratio. As the shape of the OD and OC are elliptical, this transformation results in a reduction in the dimensions. Moreover, we developed a transformer module to take advantage of its unlimited effective receptive fields to introduce global contextual information. After that, we used a feature pyramid network not only to enable fusing different scale features but also to provide a multi-scale prediction for further segmentation tasks. The performance of our method on three publicly available datasets verified the effectiveness of our approach. Our Polarformer also demonstrated cross-domain ability and had powerful performance in the few-shot scenario, which is a future direction of this research. Methodology, Y.F., Z.L., D.Y., H.G. and H.L. ; Validation, Y.F., D.Y. and H.H. ; Writing-original draft, Y.F. ; Visualization, D.Y. and H.H. ; Funding acquisition, Z.L., H.G. and H.L. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**DRISHTI-GS**} & \multicolumn{2}{c}{**REFUGE**} & \multicolumn{2}{c}{**RIM-ONE v3**} \\  & **ResNet-101** & **Eff-B4** & **ResNet-101** & **Eff-B4** & **ResNet-101** & **Eff-B4** \\ \hline Baseline & 0.881 & 0.870 & 0.873 & 0.876 & 0.868 & 0.872 \\ w/o POD & 0.887 & 0.890 & 0.884 & 0.887 & 0.882 & 0.885 \\ w/o Transformer & 0.878 & 0.882 & 0.885 & 0.880 & 0.879 & 0.882 \\ Polarformer & 0.893 & 0.901 & 0.890 & 0.893 & 0.888 & 0.895 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation study on every module of our proposed method on the DRISHTI-GS dataset, RIM-ONE v3 dataset and REFUGE dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**REFUGE**} \\  & \(\mathbf{Dice_{cup}}\) & \(\mathbf{Dice_{disc}}\) \\ \hline U-Net (R101 scratch) & 0.825 & 0.948 \\ U-Net (R101 pretrain) & 0.835 & 0.951 \\ Ours (R101 scratch) & 0.881 & 0.960 \\ Ours (R101 pretrain) & 0.892 & 0.974 \\ \hline \hline \end{tabular}
\end{table} 
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