# Exploiting Global Structure Information to Improve Medical Image Segmentation
## 4 Experiments
### Domain Robustness  
Learning the anatomical structures in medical images can enhance several aspects of segmentation models, for instance, in the form of domain robustness. To demonstrate the domain robustness of the proposed method, we trained a segmentation network by using images from a single source (i.e., domain) and tested the trained model by using images from other sources. For example, the JSRT dataset was used for training, and the trained model's performance was evaluated using the MC dataset in the case of the lung segmentation task. In general, if a network exhibits a high performance on datasets from unseen domains, the network is considered to be robust to domain shifts. We conducted similar experiments using the spinal cord dataset: images from each site, _site2_, _site3_, and _site4_, were used as the training images, and the segmentation performance was examined on images corresponding to other domains. Images from _site1_ were utilized only for testing because the dataset for _site1_ contains excessively few images to be used for training. The trained models in the previous experiments were used to examine their robustness to domain shifts. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **IOU (\(\uparrow\))** & **DSC (\(\uparrow\))** & **ACD (\(\downarrow\))** & **ASD (\(\downarrow\))** \\ \hline \multirow{4}{*}{_site2_} & U-Net & 0.793 \(\pm\) 0.006 & 0.884 \(\pm\) 0.004 & 0.585 \(\pm\) 0.031 & 0.542 \(\pm\) 0.015 \\  & ACNN & 0.792 \(\pm\) 0.004 & 0.883 \(\pm\) 0.003 & 0.592 \(\pm\) 0.028 & 0.546 \(\pm\) 0.013 \\  & SRM & 0.801 \(\pm\) 0.006 & 0.889 \(\pm\) 0.004 & 0.560 \(\pm\) 0.036 & 0.518 \(\pm\) 0.017 \\  & Proposed & **0.805 \(\pm\) 0.004** & **0.892 \(\pm\) 0.002** & **0.536 \(\pm\) 0.010** & **0.508 \(\pm\) 0.009** \\ \hline \multirow{4}{*}{_site3_} & U-Net & 0.822 \(\pm\) 0.007 & 0.900 \(\pm\) 0.005 & 0.440 \(\pm\) 0.033 & 0.401 \(\pm\) 0.013 \\  & ACNN & 0.831 \(\pm\) 0.004 & 0.905 \(\pm\) 0.003 & 0.402 \(\pm\) 0.009 & 0.382 \(\pm\) 0.007 \\  & SRM & 0.828 \(\pm\) 0.008 & 0.904 \(\pm\) 0.004 & 0.398 \(\pm\) 0.020 & 0.384 \(\pm\) 0.018 \\  & Proposed & **0.837 \(\pm\) 0.01** & **0.909 \(\pm\) 0.007** & **0.389 \(\pm\) 0.034** & **0.372 \(\pm\) 0.020** \\ \hline \multirow{4}{*}{_site4_} & U-Net & 0.853 \(\pm\) 0.004 & 0.92 \(\pm\) 0.002 & 0.423 \(\pm\) 0.015 & 0.406 \(\pm\) 0.009 \\  & ACNN & 0.856 \(\pm\) 0.002 & 0.922 \(\pm\) 0.001 & 0.413 \(\pm\) 0.008 & 0.401 \(\pm\) 0.005 \\  & SRM & 0.857 \(\pm\) 0.003 & 0.923 \(\pm\) 0.002 & 0.424 \(\pm\) 0.030 & 0.398 \(\pm\) 0.008 \\  & Proposed & **0.858 \(\pm\) 0.006** & **0.923 \(\pm\) 0.004** & **0.408 \(\pm\) 0.020** & **0.394 \(\pm\) 0.015** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of spinal cord segmentation test performance. The means and standard deviations over five runs are reported. For each dataset, the best result is shown in boldface. The domain robustness (i.e., domain generalization) performance of the segmentation models in the lung segmentation task is summarized in Table 6. Two experimental settings were considered, JSRT\(\rightarrow\)MC and MC\(\rightarrow\)JSRT. In particular, JSRT\(\rightarrow\)MC corresponds to the segmentation performances on the MC dataset for a model trained on the JSRT dataset. The last row presents the average performance over the two settings. The models trained using the proposed strategy exhibit superior performances in terms of both the overlap and distance measures, which indicates that the proposed method not only enhances the segmentation performance on the source domains, but also renders a segmentation model more robust to domain shifts. As can be seen in Figure 3, although the visual characteristics of the two datasets are considerably different, the experimental result highlights that the domain generalization performance of CNN-based segmentation models can be enhanced if we carefully design a training framework for the model to learn the anatomical structure information related to the given tasks.