# Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation
## 4 Experimental Results
### Comparisons with State-of-the-Art Methods on GlaS and MoNuSeg Datasets  
To verify the overall segmentation performance of the proposed Swin-PANet, we conduct the comparison experiment with other state-of-the-arts. Swin-PANet is compared with three types of methods for overall evaluation, including traditional U-Net [4] and three improved U-Net methods: UNet++ [14], MultiReUNet [41] and Attention U-Net [16], and three state-of-the-art Transformer complements CNN-based methods: MedT [34], TransUNet [2], UCTransNet [5], and Swin-Unet [3].  
Experimental results on GlaS and MoNuSeg datasets are shown in Table 1 which shows the quantitative performances of our proposed Swin-PANet with other methods. It can be seen that Swin-PANet achieves the best segmentation performance, such as on GlaS dataset,performance gains improvement ranges from 3.7% (5.39%) to 5.08% (8.07%) on the basis of Dice (IoU) compared with the conventional U-Net-based methods and from 1.24% (1.93%) to 3.79% (5.78%) on the basis of Dice (IoU) compared with Transformer complements CNN-based methods. In addition, on the segmentation performance on MoNuSeg dataset, our proposed Swin-PANet consistently performs the best nuclear segmentation, though there are much smaller and denser nuclei. The Dice metric of Swin-PANet is noticeably improved from 79.87% to 81.59% compared to the UCTransNet, which is the best competitor. Moreover, the IoU metric of Swin-PANet is obviously improved from 66.68% to 69.00% compared with it.  
Furthermore, the visual comparisons of nuclear segmentation on GlaS and MoNuSeg datasets are shown in Figure 5. The first two rows denote segmentation results on GlaS dataset and the last row denotes segmentation results on MoNuSeg dataset. It can be seen that our proposed Swin-PANet generates better segmentation boundaries, which are more refined and similar to the ground truth than the above Transformer-based methods. The results of quantitative comparison and observations in qualitative comparison demonstrate that our proposed Swin-PANet can also achieve excellent performance in nuclear segmentation tasks, which suggests our method has the capability of extracting much small boundary information and performing finer segmentation while keeping detailed shape information.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multicolumn{2}{c}{**GlaS**} & \multicolumn{2}{c}{**MoNuSeg**} \\ \cline{2-5}  & **Dice (\%)** & **IoU (\%)** & **Dice (\%)** & **IoU (\%)** \\ \hline U-Net (2015) & 86.34 & 76.81 & 73.97 & 59.42 \\ UNet++ (2018) & 87.07 & 78.10 & 75.28 & 60.89 \\ AttUNet (2018) & 86.98 & 77.53 & 76.20 & 62.64 \\ MRUNet (2020) & 87.72 & 79.39 & 77.54 & 63.80 \\ TransUNet (2021) & 87.63 & 79.10 & 79.20 & 65.68 \\ MedT (2021) & 86.68 & 77.50 & 79.24 & 65.73 \\ Swin-Unet (2021) & 88.25 & 79.86 & 78.49 & 64.72 \\ UCTransNet (2021) & 89.84 & 82.24 & 79.87 & 66.68 \\ UCTransNet-pre & 90.18 & 82.95 & 77.19 & 63.80 \\ Swin-PANet (ours) & 91.42 & 84.88 & 81.59 & 69.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The quantitative comparison on GlaS and MoNuSeg datasets implemented on state-of-the-art methods. For simplicity, AttUNet and MRUNet denote the above methods: Attention U-Net and MultiReUNet. UCTransNet-pre denotes UCTransNet based on pre-training.  
Figure 5: Visual comparisons of nuclear segmentation results produced by different methods on GlaS and MoNuSeg datasets. The first two rows represent segmentation results on GlaS dataset and the last row represents segmentation results on MoNuSeg dataset. The regions highlighted by red boxes show Swin-PANet performs better segmentation than other state-of-the-art methods and the results of segmentation are more similar to the ground truth.