# Exploiting Global Structure Information to Improve Medical Image Segmentation
## 4 Experiments
### Spinal Cord Segmentation Result  
The spinal cord gray matter challenge dataset [17] was considered to evaluate the spinal cord segmentation performance of the proposed method. This dataset, which is composed of 3D MRI images, was cut cross-sectionally to allow the use of two-dimensional images in the experiment. Images without the ground-truth label were not used. Eventually, we considered 30, 113, 177, and 134 images from _site1_, _site2_, _site3_, and _site4_, respectively. Images from _site1_ were not used for training due to the small number of images. The dataset corresponding to each site was split as follows: 65% for training, 15% for validation, and 20% for testing. All the images were center cropped at 128 \(\times\) 128 pixels. The architectures of the segmentation network and autoencoder are similar to those in the lung segmentation experiment except for the number of layers and kernels: we used four times more kernels and removed one block in each encoder and decoder (i.e., \(f_{4}\) and \(g_{4}\) in Tables 1 and 2) to build a better baseline. The hyperparameters involved in each method were determined through a validation process: For the ACNN, the weight for the shape regularization loss was set as 0.001, and for the SRM, the weights for the shape regularization loss and reconstruction loss were set as 0.01 and 0.001, respectively. The weight \(\lambda\) for the proposed method was 1.0. All the methods were trained using the AdamP optimizer [30] for 120 epochs owing to the more stable training progress. The learning rate and weight decay parameter were set as 0.01 and 0.0001, respectively. To compare with stronger baselines, data augmentation strategies were adopted, including random adjustment of the brightness and contrast in the range of 0.6 to 1.4. The other experimental settings were the same as those in the lung segmentation experiment. To compare the performance, the mean and standard deviation of the performance metrics over five runs are reported in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **IOU(\(\uparrow\))** & **DSC(\(\uparrow\))** & **ACD(\(\downarrow\))** & **ASD(\(\downarrow\))** \\ \hline U-Net & 0.955 \(\pm\) 0.002 & 0.977 \(\pm\) 0.001 & 1.116 \(\pm\) 0.083 & 0.745 \(\pm\) 0.017 \\ Proposed-BI & 0.958 \(\pm\) 0.002 & 0.978 \(\pm\) 0.001 & 0.976 \(\pm\) 0.054 & 0.705 \(\pm\) 0.010 \\ Proposed-UN & **0.959 \(\pm\) 0.002** & **0.979 \(\pm\) 0.001** & **0.936 \(\pm\) 0.052** & **0.691 \(\pm\) 0.013** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study pertaining to the effect of controlling gradient flows. The best result is shown in boldface. Similar to the results of the lung segmentation task, the ACNN and SRM outperformed the baseline U-Net, and the proposed method outperformed the comparison methods in terms of all metrics across the datasets from site2, site3, and site4. Notably, the distance metrics, ACD and ASD, were greatly improved, as in the previous experiment. For example, the ASD value of the proposed method on site2 was 0.372, 7.2% lower than the baseline and 2.6% lower than the second-best model ACNN. The rows in Figure 5 show the predicted images from the segmentation methods for _site2_, _site3_, and _site4_, in order. The red solid line is the ground-truth label, and the green area represents the predicted result from the segmentation network. From the left part showing the comparison results, we can observe that the predictions from U-Net contain false positives located far from the ground-truth in several cases, although the other methods provide better segmentation results. Among these methods, the proposed method can realize more precise segmentation, especially in the case shown in the second row. These results demonstrate that the proposed method can more effectively learn the anatomical structure information than the comparison methods, resulting in better segmentation results.