# Exploiting Global Structure Information to Improve Medical Image Segmentation
## 4 Experiments
### Domain Robustness
The visualizations of several segmentation results are presented in Figure 6. The red solid line and blue shaded area represent the ground-truth and segmentation outputs, \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Setting** & **Method** & **IOU (\uparrow)** & **DSC (\uparrow)** & **ACD (\downarrow)** & **ASD (\downarrow)** \\ \hline \multirow{4}{*}{JSRT\(\rightarrow\)MC} & U-Net & \(0.897\pm 0.008\) & \(0.943\pm 0.004\) & \(4.088\pm 1.053\) & \(1.377\pm 0.171\) \\  & ACNN & \(0.904\pm 0.006\) & \(0.947\pm 0.003\) & \(2.528\pm 0.500\) & \(1.112\pm 0.080\) \\  & SRM & \(0.902\pm 0.005\) & \(0.946\pm 0.002\) & \(3.481\pm 0.753\) & \(1.272\pm 0.111\) \\  & Proposed & \(0.924\pm 0.004\) & \(0.960\pm 0.002\) & \(2.101\pm 0.639\) & \(1.032\pm 0.095\) \\ \hline \multirow{4}{*}{MC\(\rightarrow\)JSRT} & U-Net & \(0.934\pm 0.001\) & \(0.966\pm 0.001\) & \(1.684\pm 0.055\) & \(0.987\pm 0.010\) \\  & ACNN & \(0.936\pm 0.001\) & \(0.967\pm 0.001\) & \(1.451\pm 0.049\) & \(0.945\pm 0.016\) \\ \cline{1-1}  & SRM & \(0.935\pm 0.001\) & \(0.967\pm 0.001\) & \(1.580\pm 0.037\) & \(0.965\pm 0.011\) \\ \cline{1-1}  & Proposed & \(0.938\pm 0.002\) & \(0.968\pm 0.001\) & \(1.388\pm 0.038\) & \(0.924\pm 0.012\) \\ \hline \multirow{4}{*}{Average} & U-Net & \(0.916\) & \(0.955\) & \(2.886\) & \(1.182\) \\ \cline{1-1}  & ACNN & \(0.920\) & \(0.957\) & \(1.990\) & \(1.029\) \\ \cline{1-1}  & SRM & \(0.919\) & \(0.957\) & \(2.531\) & \(1.119\) \\ \cline{1-1}  & Proposed & \(0.931\) & \(0.964\) & \(1.745\) & \(0.978\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the domain robustness on lung segmentation. The best result on averaging over two settings is shown in boldface. Figure 5: Visualization of the spinal cord segmentation results on test samples from _site2_ (**top**), _site3_ (**middle**), and _site4_ (**bottom**). The left part shows the results of each method, and the right part presents the results from \(f_{DAE}\to g_{DAE}\) and \(f_{SEG}\to g_{DAE}\) of the proposed method. The green color represents the segmentation results of each method, and the ground-truth is in red. respectively. The existing approaches including the baseline U-Net are sensitive to even a small degree of domain shift, and this phenomenon cannot be simply resolved by applying data augmentation techniques such as random adjustments of the brightness and contrast. For the spinal cord segmentation task, three experimental settings were considered: _site2_\(\rightarrow\)others, _site3_\(\rightarrow\)others, and _site4_\(\rightarrow\)others. For models trained with each source domain, we evaluated the segmentation performance on the images from all other domains. The proposed method exhibited a higher generalization capability on unseen domains, as presented in Table 7. Except for the setting in which the source domain was _site3_, the proposed method achieved better segmentation results on other domains, especially in terms of the distance metrics. For example, when the source domain was _site4_, the ACD value averaged over other domains including _site1_, _site2_, and _site3_ was improved by 14.8% compared to the second-best model SRM. According to the averaged performance over three experimental settings, the baseline U-Net, ACNN, and the proposed method showed a similar segmentation performance in terms of the overlap measures, although the distance performances of the proposed method were significantly improved. Specifically, the average ACD value for the proposed method was 0.667, corresponding to a 9.7% improvement over the baseline U-Net. Figure 6: Segmentation results on the lung segmentation task obtained from the models trained using the (**a**) JSRT and (**b**) MC datasets, respectively. The blue color represents the segmentation results, and the ground-truth is in red. Figure 7 shows the segmentation results obtained using each model from the test images in other domains not used for training. Figure 7a-c show the results from the models trained with _site2_, _site3_, and _site4_ as the source domains, respectively.