# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 4 Discussion
### Differences in Data Augmentation

Table 4: Representative scores of the tested data-augmentation configurations. Model experiences in bold are those retained for computed augmentation metrics in Tables 1 and 2. When not indicated, 7 rotation steps were applied by default, as shown in Figure 7.  
Figure 7: Average metric curves over different data-augmentation configurations. Nonaugmented and best average configuration performance-wise (presented in Tables 1, 4 and 6) are emphasized in bold. with two stages. In this case, flipping as a separate processing group in parallel to image alterations may contribute to decreased performances, as opposed to being applied in the third stage. This is particularly more visible with the _ENet + Stages 1 and 2 Aug_ configuration, which also used seven rotation steps. Lowering the number of rotations in the _ENet + Stages 1 and 2 Aug 3 Rotation Steps_ configuration improved metrics in both cases, although not up to the best-achieved results. On the other hand, experiments using data augmentation solely based on rotations with moderate intensity changes led to the best results achieved with the four-chamber dataset. While the average results are lower than those obtained in the two-chamber dataset, the improvements tend to be more pronounced in comparison. With the exception of the aforementioned _ENet + Stages 1 and 2 Aug_ configuration, a clear increase is observed over the Dice metric with all augmentation configurations tried on the four-chamber dataset. This difference between the results with both augmented datasets can be attributed to the higher variability of the encountered situations present in the four-chamber dataset, leading to more configurations with potential image-quality variations. Overall, this point supports the conclusions shared by [PERSON] et al. (2018) and [PERSON] et al. (2019), where a mix of both shape modification and image-quality alterations led to improved segmentation. Another factor contributing to the observed difference in improvement is the presence of difficult segmentation classes with more complex forms, as well as the inconsistently present AO class. Further experimentation was carried out by comparing the average Dice improvement between different dataset configurations, as shown in Figure 8. Random examinations were chosen to generate smaller datasets, comprising 250 and 500 ED/ES image pairs, respectively, for both two-chamber and four-chamber views. The corresponding test sets contained 25 and 50 examinations/pairs, respectively. For this experiment, the _ENet + Stage 1 Aug_ configuration was applied to both views. Data augmentation demonstrated a more pronounced Dice improvement over the two-chamber dataset with a limited amount of data. However, as the number of examinations increased, this improvement became less noticeable, contrary to what can be inferred with the four-chamber dataset in Figure 8. While the quantity of examinations differs between datasets for reasons provided in Section 2.1, this additional exper  
Figure 8: Comparison of Dice score improvement between 2- and 4-chamber views with different amounts of data. Experiments were conducted with shared dataset sizes of 250 and 500 examinations in addition to originally available data. it is plausible to expect a similar trend if the number of examinations was the same across both datasets.