# Reverse-Net: Few-Shot Learning with Reverse Teaching for Deformable Medical Image Registration
## 5 Results
### Ablation Experiment  
We devised ablation experiments to validate the contribution of reverse teaching networks. We conducted experiments using brain MR data to evaluate the registration performance of the model using the average DSC, the percentage of non-positive values in the determinant of the Jacobi matrix of the deformation field, and the average alignment accuracy per image.  
The results of the ablation experiments are shown in Table 1. The structural information of different unlabeled images is passed to the warped, labeled images through the deformation field formed by the pixel alignment network, and image A and the warped, labeled images with rich structural knowledge will provide this rich structural knowledge to the reverse teaching network. The above operation can provide quality data for the subsequent pixel alignment network. Compared with Pre-net, the structural information brings 0.76% DSC and 4.47% precision improvement to our model. Compared with Cof-net, the structural information brings 3.5% DSC and 3.82% precision improvement to our model. In addition, as shown in Figure 7. Compared with the other two models, our model is richer in detail. The above results show that the reverse teaching network improves the registration performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **DSC (\%)** & **Precision (\%)** & **Det.Jac (\%)** \\ \hline Cof-net [46] & 81.70 \(\pm\) 3.71 & 90.59 \(\pm\) 3.05 & 0.006 \(\pm\) 0.002 \\ Pre-net & 84.44 \(\pm\) 0.67 & 89.94 \(\pm\) 4.27 & **0.002 \(\pm\) 0.001** \\ Un-reverse-net & 83.29 \(\pm\) 0.15 & 89.45 \(\pm\) 1.23 & 0.004 \(\pm\) 0.012 \\ Reverse-net & **85.20 \(\pm\) 1.18** & **94.41 \(\pm\) 0.89** & 0.004 \(\pm\) 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative results of ablation experiments. The numbers following the \(\pm\) indicate the standard deviation.  
Figure 6: ROI boundary enhancement results on CIT168 and OASIS.  
In addition, we investigated the effect of different numbers of segmented labels on the registration performance. As shown in Figure 8. When having one segmented label, the model decreases 2.33% on DSC compared with Pre-net. When having three labels, the model improves 0.41% on DSC compared with Pre-net. This shows that our reverse teaching network can improve the registration performance of the model. As the number of labels increases, our model is further enhanced. When the number of labels is increased to five, the reverse-net improves 0.76% on DSC compared with Pre-net. This further shows that our network has improved the ability of the border enhancement network sensing boundary through reverse teaching.  
Finally, we evaluated the effect of the model without segmented label participation. The results of quantitative analysis are shown in Table 1. Compared to Cof-net, our model improves 1.59% on DSC and decreases 0.002 on Det.Jac. Compared to VoxelMorph, our model improves 2.45% on DSC and decreases 0.002 on Det.Jac. The above results show the superiority of our model design. When labels are involved, the model results are even better.