# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 3 Results
### Influence of Data Augmentation  
Both architectures were trained on augmented datasets following procedures described previously in Section 2.3.2. Using this hierarchical strategy, several configurations were tested, involving varying degrees of rotation applied to all of the generated groups of normal and altered images. In the lightest configuration, only rotation-based data aug \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models** +} & \multicolumn{3}{c}{**Dice**} & \multicolumn{3}{c}{**Dice**} & \multicolumn{3}{c}{**Handcrafted FID (mean)**} \\ \cline{2-13}  & \multicolumn{2}{c}{**ED**} & \multicolumn{2}{c}{**ES**} & \multicolumn{2}{c}{**Mean Difference**} & \multicolumn{2}{c}{**ED**} & \multicolumn{2}{c}{**ES**} & \multicolumn{2}{c}{**Mean Difference**} \\ \cline{2-13}  & **DA** & **No DA** & **DA** & **No DA** & **Rel(\%)** & **Abs(\%)** & **DA** & **No DA** & **DA** & **ND(\%)** & **Abs(\%)** \\ \hline ENet 2.00\% test & 0.98\(\pm\) 0.004 & 0.90\(\pm\) 0.072 & 0.99\(\pm\) 0.009 & 0.81\(\pm\) 0.009 & 0.85 & 0.99 & **5.88\(\pm\) 1.37** & 2.79\(\pm\) 1.78 & **5.71\(\pm\) 2.78** & 6.47\(\pm\) 3.47 & 1.49 & 21.35 \\ ENet 2.00\% test & 0.500 \(\pm\) 0.005 & 0.900 \(\pm\) 0.025 & 0.98\(\pm\) 0.004 & 0.914 \(\pm\) 0.002 & 0.95 & 0.96 & **5.80\(\pm\) 4.32** & 6.22 \(\pm\) 5.24 & **6.34\(\pm\) 3.99** & 6.65 \(\pm\) 5.08 & 0.42 & 6.52 \\ ENet 4.00\% test & 0.802 \(\pm\) 0.061 & 0.800 \(\pm\) 0.003 & 0.988 \(\pm\) 0.008 & 0.899 \(\pm\) 0.002 & 1.90 & 2.15 & **8.87\(\pm\) 1.85** & 11.75 \(\pm\) 2.51 & 7.76\(\pm\) 4.27 & 11.58 \(\pm\) 2.03 & 3.45 & 29.58 \\ ENet 4.00\% test & 0.808 \(\pm\) 0.003 & 0.806 \(\pm\) 0.004 & 0.807 \(\pm\) 0.004 & 0.920 \(\pm\) 0.002 & 1.75 & 2.02 & **7.33\(\pm\) 1.80** & 0.94 \(\pm\) 1.54 & **6.48 \(\pm\) 1.30** & 8.82 \(\pm\) 1.37 & 1.56 & 14.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average metrics scores at end-diastolic (ED) and end-systolic (ES) timeframes, with and without hierarchical data augmentation. mentation was performed by using \(R_{3}\), including rotations ranging from \(-60^{\circ}\) to \(+60^{\circ}\) over seven steps, along with the preliminary intensity alteration. This experiment is denoted as _ENet + Stage 1 Aug_ in Table 4 and _7_Rot_S1_ in Figure 7, respectively. The heaviest configurations involved all three hierarchical stages of augmentation. The corresponding experiments are later referred to as _ENet + FullAug 11 rotation steps_ and _ENet + FullAug_ in Section 4.2 and are associated with _11_Rot_S1_S2_S3_ and _7_Rot_S1_S2_S3_ in Figure 7, respectively. In some intermediate augmentation configurations, the third stage was removed. Instead, an additional frame-alteration group focused solely on image flipping is placed in the second stage. This augmentation was performed in parallel with the image-quality-alteration groups instead of being applied to them sequentially, making it mostly geometric in nature. The number of rotations was also reduced to mitigate the overfitting issues arising from data similarity. With the lightest of these intermediate configurations, the rotation configurations \(R_{1}\) and \(R_{2}\) were used, with \(r=3\) rotations. The experiments involving two-stage augmentation are referred to as _ENet + Stages 1 and 2 Aug_ and _ENet + Stages 1 and 2 Aug 3 Rotation Steps_ in Section 4.2. They correspond to the labels _7_Rot_S1_S2_ and _3_Rot_S1_S2_ in Figure 7, respectively. Using the heaviest data-augmentation process, 51,150 and 77,550 training images were generated from the two- and four-chamber datasets, respectively, compared to 930 and 1410 with no data augmentation. While most of the configurations referred above were shared between databases, the rotation range was settled at a wider amplitude of 45\({}^{\circ}\) for the four-chamber dataset compared to the 30\({}^{\circ}\) two-chamber dataset in configuration _Stages 1 and 2 Aug 3 Rotation Steps_. This change was made as acquisitions showed more anatomical variation over the four-chamber dataset, as seen in Figure 9c, for example.