# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 4 Discussion
### Comparison to UNet  
The effect of data augmentation was also assessed via training by using the UNet architecture, which is one of the most widely used architectures in the medical-imaging domain. This experiment involved the best data-augmentation configurations discussed in Section 4.2. The results of the comparison between ENet and UNet are reported in Table 6.  
Better performances were obtained with UNet over ENet for the four-chamber dataset. This can be attributed to the difference in the depth and learnable parameters between the two networks. The ENet architecture is simpler with lesser learnable parameters and employs various techniques to reduce the computational complexity of the operations it employs. On the contrary, the ENet architecture was able to achieve better results on the two-chamber dataset, especially for the Hausdorff distance scores. The observed difference can be explained by the following hypothesis. As the ENet architecture is shallower than UNet, learning simpler structures, as present in two-chamber images, is easier than learning complex structures, as present in four-chamber images. Nevertheless, the performances of ENet stay acceptable despite the model's smaller size compared to UNet. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Dice**} & \multicolumn{4}{c}{**Hausdorff (mm)**} \\ \cline{2-7}  & \multicolumn{1}{c}{**ED**} & \multicolumn{1}{c}{**ES**} & \multicolumn{1}{c}{**ED**} & \multicolumn{1}{c}{**ES**} \\ \cline{2-7}  & **DA** & **No DA** & **DA** & **No DA** & **No DA** & **DA** & **No DA** \\ \hline ENet 2 cav test & 0.910 \(\pm\) 0.034 & 0.900 \(\pm\) 0.072 & **0.919 \(\pm\) 0.039** & 0.912 \(\pm\) 0.059 & **5.28 \(\pm\) 2.17** & 7.79 \(\pm\) 1.798 & **5.71 \(\pm\) 2.76** & 6.17 \(\pm\) 3.87 \\ ENet 2 cav val & 0.903 \(\pm\) 0.065 & 0.904 \(\pm\) 0.055 & 0.916 \(\pm\) 0.040 & 0.914 \(\pm\) 0.042 & **5.90 \(\pm\) 4.52** & 6.23 \(\pm\) 5.24 & **6.14 \(\pm\) 3.69** & 6.65 \(\pm\) 5.08 \\ \hline UNet 2 cav test & **0.914 \(\pm\) 0.046** & 0.897 \(\pm\) 0.091 & 0.913 \(\pm\) 0.077 & 0.902 \(\pm\) 0.112 & 8.63 \(\pm\) 15.69 & 10.96 \(\pm\) 22.74 & 8.66 \(\pm\) 20.37 & 13.59 \(\pm\) 32.45 \\ UNet 2 cav val & **0.908 \(\pm\) 0.066** & 0.889 \(\pm\) 0.093 & **0.919 \(\pm\) 0.057** & 0.901 \(\pm\) 0.087 & 7.53 \(\pm\) 12.32 & 14.50 \(\pm\) 33.07 & 8.06 \(\pm\) 12.52 & 13.73 \(\pm\) 28.87 \\ \hline ENet 4 cav test & 0.902 \(\pm\) 0.061 & 0.880 \(\pm\) 0.080 & 0.905 \(\pm\) 0.068 & 0.889 \(\pm\) 0.082 & 8.87 \(\pm\) 13.65 & 11.75 \(\pm\) 21.51 & 7.56 \(\pm\) 9.27 & 11.58 \(\pm\) 20.63 \\ ENet 4 cav val & 0.906 \(\pm\) 0.053 & 0.886 \(\pm\) 0.084 & 0.907 \(\pm\) 0.060 & 0.892 \(\pm\) 0.082 & 7.33 \(\pm\) 9.08 & 9.08 \(\pm\) 15.14 & 8.45 \(\pm\) 13.01 & 9.82 \(\pm\) 16.37 \\ \hline UNet 4 cav test & **0.918 \(\pm\) 0.053** & 0.998 \(\pm\) 0.077 & **0.915 \(\pm\) 0.064** & 0.998 \(\pm\) 0.090 & **7.51 \(\pm\) 10.33** & 9.07 \(\pm\) 14.21 & **7.20 \(\pm\) 9.92** & 10.34 \(\pm\) 16.70 \\ UNet 4 cav val & **0.920 \(\pm\) 0.054** & 0.900 \(\pm\) 0.095 & **0.919 \(\pm\) 0.055** & 0.900 \(\pm\) 0.095 & **6.02 \(\pm\) 6.78** & 9.15 \(\pm\) 22.81 & **7.90 \(\pm\) 10.69** & 10.79 \(\pm\) 29.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average metric scores for ENet and UNet at end-diastolic and end-systolic phases, with and without hierarchical data augmentation. In bold are the best-reported results between architectures. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Aorta-Detection Metrics**} & **Validation Set (200 Examinations)** & **Test Set (102 Examinations)** \\ \cline{2-4}  & **ED** & **ES** & **ED** & **ES** \\ \hline
**True positive** & 41 & 42 & 29 & 33 \\
**True negative** & 143 & 134 & 65 & 64 \\
**False positive** & 14 & 21 & 5 & 4 \\
**False negative** & 2 & 3 & 3 & 1 \\ \hline
**Sensitivity** & 0.953 & 0.933 & 0.906 & 0.971 \\
**Specificity** & 0.911 & 0.865 & 0.929 & 0.941 \\ \hline \hline \end{tabular}
\end{table}