# Exploiting Global Structure Information to Improve Medical Image Segmentation
## 4 Experiments
### Lung Segmentation Result
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Kernel Size** & **Stride** & **Kernels** & **Feature Map** \\ \hline \multirow{3}{*}{\(f_{1}\)} & conv & \(3\times 3\) & \(1\times 1\) & 16 & 256 \(\times\) 256 \(\times\) 16 \\  & conv & \(2\times 2\) & \(2\times 2\) & & 128 \(\times\) 128 \(\times\) 16 \\ \hline \multirow{3}{*}{\(f_{2}\)} & conv & \(3\times 3\) & \(1\times 1\) & 32 & 128 \(\times\) 128 \(\times\) 32 \\  & maxpool & \(2\times 2\) & \(2\times 2\) & & 64 \(\times\) 64 \(\times\) 32 \\ \hline \multirow{3}{*}{\(f_{3}\)} & conv & \(3\times 3\) & \(1\times 1\) & 64 & 64 \(\times\) 64 \(\times\) 64 \\  & maxpool & \(2\times 2\) & \(2\times 2\) & & 32 \(\times\) 32 \(\times\) 64 \\ \hline \multirow{3}{*}{\(f_{4}\)} & conv & \(3\times 3\) & \(1\times 1\) & 128 & 32 \(\times\) 32 \(\times\) 128 \\  & maxpool & \(2\times 2\) & \(2\times 2\) & & 16\(\times\)16\(\times\)128 \\ \hline \multirow{3}{*}{\(z\)} & conv & \(3\times 3\) & \(1\times 1\) & 256 & 16\(\times\)16\(\times\)256 \\  & conv & \(2\times 2\) & \(2\times 2\) & 256 & 32 \(\times\) 32 \(\times\) 256 \\ \hline \multirow{3}{*}{\(g_{4}\)} & deconv & \(2\times 2\) & \(2\times 2\) & 128 & 64 \(\times\) 64 \(\times\) 128 \\  & conv & \(3\times 3\) & \(1\times 1\) & 64 & 64 \(\times\) 64 \(\times\) 64 \\ \hline \multirow{3}{*}{\(g_{3}\)} & deconv & \(2\times 2\) & \(2\times 2\) & 64 & 128 \(\times\) 128 \(\times\) 64 \\  & conv & \(3\times 3\) & \(1\times 1\) & 32 & 128 \(\times\) 128 \(\times\) 32 \\ \hline \multirow{3}{*}{\(g_{2}\)} & deconv & \(2\times 2\) & \(2\times 2\) & 32 & 256 \(\times\) 256 \(\times\) 32 \\  & conv & \(3\times 3\) & \(1\times 1\) & 16 & 256 \(\times\) 256 \(\times\) 16 \\ \hline \multirow{3}{*}{\(g_{1}\)} & deconv & \(1\times 1\) & \(1\times 1\) & 1 & 256 \(\times\) 256 \(\times\) 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed architecture of the segmentation network for lung segmentation, representing a kernel size, stride, the number of kernels, and size of output feature map of each layer. The visualization results are presented in Figure 4. The first and second rows show the segmentation results on the JSRT and MC dataset, respectively. The red solid line represents the ground-truth label, and the green area corresponds to the predicted result from the segmentation network. The left part shows the segmentation results of each method. The base U-Net tends to inaccurately predict the lung regions, and the reflection of the anatomical information in the network helps achieve better segmentation of the lung regions. Notably, the approach to use the anatomical information through the proposed strategy helped achieve the most accurate segmentation result. To gain further insight into the proposed method, the reconstruction results from the trained DAE (i.e., \(f_{DAE}\to g_{DAE}\)) and segmentation results from the combination of segmentation encoder and DAE decoder (i.e., \(f_{SEG}\to g_{DAE}\)) are also depicted (see the right part of Figure 4). Here, we examined the reconstruction capability of DAE although it is not used during inference. The DAE reconstructs the input labels as well as expected since the reconstruction of binary lung masks is an easy task. The results from \(f_{SEG}\to g_{DAE}\) are noteworthy: the features from \(f_{SEG}\) can be successfully decoded by \(g_{DAE}\). It implies that \(f_{SEG}\) can embed an input image into the anatomical feature space modeled by the DAE, and thereby, \(g_{DAE}\) can produce good segmentation results based on those features.