# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 4 Discussion
### Differences in Data Augmentation  
As mentioned in Section 2.3, several augmentation configurations were implemented with varying degrees of rotation, flipping and the inclusion or exclusion of image-quality alterations through noise and intensity variations. Different behaviors were observed for each dataset. From Table 4 and Figure 7, it is evident that the best performances are achieved with the ENet architecture trained on the two-chamber augmented dataset generated for the _ENet_ + _FullAug_ configuration. This configuration corresponds to the full hierarchical pipeline data augmentation schematized in Figure 3. The primary contributing factor here was the number of generated rotation steps, as an excessive number of rotations led to lower performances, as was seen with the _ENet_ + _FullAug_ 11 Rotation Steps_ configuration. The _ENet_ + _FullAug_ configuration, comprising seven rotation steps, yielded the best results. Apart from rotation, the strategy used for flipping plays an equally important role in improving the segmentation. This was observed in the augmentation configuration \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Validation Set (155 Rankinations)**} & \multicolumn{4}{c}{**Test Set (08 Rankinations)**} \\ \cline{2-10}
**2-Channel Models** & \multicolumn{2}{c}{**End Displace**} & \multicolumn{2}{c}{**End Syndic**} & \multicolumn{2}{c}{**End Displace**} & \multicolumn{2}{c}{**End Syndic**} \\ \cline{2-10}  & **Diver** & **Headized (mm)** & **Diver** & **Headized (mm)** & **Diver** & **Headized (mm)** & **Diver** & **Headized (mm)** \\ \hline
**ENet Noings** & **0.904 + 0.055** & 6.23 + 5.24 & 0.914 + 0.002 & 6.45 + 5.08 & 0.900 + 0.072 & 2.79 + 1.78 & 0.992 + 0.059 & 6.47 + 3.36 \\ \hline ENet - Skipet 1 Aug & 0.909 + 0.057 & 5.94 + 4.25 & 0.914 + 0.008 & **6.49 + 3.79** & 0.920 + 0.027 & 5.74 + 2.07 & 0.955 + 0.038 & 5.75 + 2.20 \\ \hline ENet - Skipet 1 and 2 Aug 3 Reduction Steps & 0.900 + 0.066 & 6.900 + 4.06 & 6.900 + 4.20 & 0.900 + 5.42 & 0.914 + 0.038 & 5.75 + 2.59 \\ \hline ENet - Skipet 1 and 2 Aug & 0.91 + 0.067 & 7.92 + 1.78 & 0.911 + 0.001 & 6.73 + 4.16 & 0.970 + 0.007 & 7.00 + 1.027 & 0.990 + 0.005 & 6.74 + 16.25 \\ \hline ENet + FullAug 11 Rotation Steps & 0.994 + 0.069 & 7.30 + 1.42 & 0.914 + 0.001 & 6.35 + 4.50 & 0.899 + 0.040 & 6.27 + 6.35 & 0.955 + 0.037 & 6.40 + 5.77 \\ \hline
**ENet + FullAug** & 0.93 + 0.065 & **5.90 + 5.52** & **0.918 + 0.000** & 6.14 + 3.09 & **0.900 + 0.004** & **5.38 + 1.07** & **0.991 + 0.009** & **5.71 + 2.76** \\ \hline \multicolumn{10}{c}{**Validation Set (200 Enumeration)**} & \multicolumn{2}{c}{**Total Set (102 Enumeration)**} \\ \cline{2-10}
**4-Channel Models** & \multicolumn{2}{c}{**End Displace**} & \multicolumn{2}{c}{**End Syndic**} & \multicolumn{2}{c}{**End Displace**} & \multicolumn{2}{c}{**End Displace**} \\ \cline{2-10}  & **Diver** & **Headized (mm)** & **Diver** & **Headized (mm)** & **Diver** & **Headized (mm)** \\ \hline
**ENet Noings** & 0.886 + 0.084 & 9.98 + 15.54 & 0.892 + 0.002 & 9.82 + 16.37 & 0.880 + 0.080 & 11.75 + 21.51 & 0.889 + 0.002 & 11.58 + 20.63 \\ \hline
**ENet + Skipet 1 Aug & **0.896 + 0.083** & **7.33 + 5.08** & **0.907 + 0.000** & 8.45 + 13.01 & 0.902 + 0.061 & **8.87 + 10.48** & 0.985 + 0.008 & **7.56 + 9.27** \\ \hline ENet + Skipet 1 and 2 Aug 3 Reduction Steps & 0.902 + 0.064 & 7.65 + 6.53 & 0.904 + 0.064 & **8.49 + 15.04** & 0.901 + 0.059 & 8.97 + 10.53 & 0.953 + 0.005 & 9.93 + 15.03 \\ \hline ENet + Skipet 1 and 2 Aug & 0.901 + 0.067 & 6.82 + 12.6 & 0.895 + 0.067 & 6.14 + 16.61 & 0.891 + 0.066 & 6.03 + 15.30 & 0.888 + 0.075 & 13.11 + 23.63 \\ \hline ENet + FullAug 11 Rotation Steps & 0.903 + 0.063 & 9.19 + 15.56 & 0.905 + 0.066 & 10.11 + 17.03 & 0.999 + 0.061 & 123.2 + 21.57 & 0.987 + 0.005 & 10.72 + 19.98 \\ \hline ENet + FullAug & 0.903 + 0.063 & 8.25 + 17.2 & 0.907 + 0.064 & 6.90 + 13.04 & **0.903 + 0.060** & 10.37 + 5.58 & **0.907 + 0.000** & 9.66 + 14.72 \\ \hline \hline \end{tabular}
\end{table}