# Exploiting Global Structure Information to Improve Medical Image Segmentation
## 4 Experiments
### Lung Segmentation Result
\begin{table}
\begin{tabular}{c 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 & \(3\times 3\) & \(1\times 1\) & 16 & 256 \(\times\) 256 \(\times\) 16 \\  & maxpool & \(2\times 2\) & \(2\times 2\) & & 128 \(\times\) 128 \(\times\) 16 \\ \hline \multirow{3}{*}{\(f_{2}\)} & conv & \(3\times 3\) & \(1\times 1\) & 32 & 128 \(\times\) 128 \(\times\) 32 \\  & 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 \\  & 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 \\  & 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}{*}{\(h\)} & conv & \(3\times 3\) & \(1\times 1\) & 256 & 16 \(\times\) 16 \(\times\) 256 \\  & conv & \(3\times 3\) & \(1\times 1\) & 256 & 16 \(\times\) 16 \(\times\) 256 \\ \hline \multirow{3}{*}{\(g_{4}\)} & deconv & \(2\times 2\) & \(2\times 2\) & 128 & 32 \(\times\) 32 \(\times\) 128 \\  & conv & \(3\times 3\) & \(1\times 1\) & 128 & 32 \(\times\) 32 \(\times\) 128 \\  & conv & \(3\times 3\) & \(1\times 1\) & 128 & 32 \(\times\) 32 \(\times\) 128 \\ \hline \multirow{3}{*}{\(g_{3}\)} & deconv & \(2\times 2\) & \(2\times 2\) & 64 & 64 \(\times\) 64 \(\times\) 64 \\  & conv & \(3\times 3\) & \(1\times 1\) & 64 & 64 \(\times\) 64 \(\times\) 64 \\  & conv & \(3\times 3\) & \(1\times 1\) & 64 & 64 \(\times\) 64 \(\times\) 64 \\ \hline \multirow{3}{*}{\(g_{2}\)} & deconv & \(2\times 2\) & \(2\times 2\) & 32 & 128 \(\times\) 128 \(\times\) 32 \\  & conv & \(3\times 3\) & \(1\times 1\) & 32 & 128 \(\times\) 128 \(\times\) 32 \\  & conv & \(3\times 3\) & \(1\times 1\) & 32 & 128 \(\times\) 128 \(\times\) 32 \\ \hline \multirow{3}{*}{\(g_{1}\)} & deconv & \(2\times 2\) & \(2\times 2\) & 16 & 256 \(\times\) 256 \(\times\) 16 \\  & conv & \(3\times 3\) & \(1\times 1\) & 16 & 256 \(\times\) 256 \(\times\) 16 \\ \hline \multirow{3}{*}{\(g_{2}\)} & conv & \(3\times 3\) & \(1\times 1\) & 1 & 256 \(\times\) 256 \(\times\) 1 \\  & conv & \(3\times 3\) & \(1\times 1\) & 16 & 256 \(\times\) 256 \(\times\) 1 \\ \hline \multirow{3}{*}{\(g_{1}\)} & deconv & \(2\times 2\) & \(2\times 2\) & 16 & 256 \(\times\) 256 \(\times\) 16 \\  & conv & \(3\times 3\) & \(1\times 1\) & 16 & 256 \(\times\) 256 \(\times\) 16 \\ \hline \multirow{3}{*}{\(g_{2}\)} & conv & \(1\times 1\) & \(1\times 1\) & 1 & 256 \(\times\) 256 \(\times\) 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed architecture of the autoencoder network for lung segmentation, representing a kernel size, stride, the number of kernels, and size of output feature map of each layer.