## 4 Discussion  
In this study, we proposed an image processing-based data augmentation algorithm dedicated to the segmentation of the myocardium on DE MRI. The method can create synthetic infarction cases using prior information extracted from an external dataset. The algorithm works under two possible scenarios: fixed and adaptive. In the first one, the training dataset is enlarged at a given amount, whereas in the second, feedback from the model is obtained while training.  
The first set of experiments was performed under the single-modality approach. This was performed using a traditional UNet with batch normalization layers as a segmentation model. Afterwards, the data augmentation algorithm was applied at fixed rates, namely, 50%, 100%, and 200%. It could be noticed that the segmentation performance on the myocardium was higher as more artificial data were available. The best result is obtained at a rate of 200%. The adaptive data augmentation strategy also offered an increase in the Dice score of the myocardium, but this was less than the increase generated with the 200% strategy.  
Similarly, the results of the multi-modality approach show that the segmentation is consistently improved when increasing the percentage of data augmentation up to 100%. Once again, the results were also improved with the adaptive data augmentation strategy, but to a smaller extent when comparing it to the 100% dataset enlargement.  
In both single- and multi-modality scenarios, it was observed that the models have a good generalization capability. Furthermore, it was demonstrated that adding the data augmentation helps to better delineate the myocardium in two ways: it either completes missed contours (top part of Figure 7) or includes MI areas that were previously missed (left part of Figure 8). Although the improvement in Dice score might seem small when seen in the general view, the produced segmentation masks are much more consistent and clinically relevant. It was also shown in Figure 10 that, when focused only on cases related to MI (the target group for our study), the quantitative improvement is more significant, and most of the outliers (corresponding to the poorly segmented cases) are eliminated. These improvements also prove that when models are fed with enough synthetic MI, they can better segment the myocardium.  
There were a few cases in which the segmentation performance was lower after applying the data augmentation (bottom and right parts of Figures 7 and 8, respectively). In these cases, the myocardium area becomes thicker than it should, which means that left ventricle cavity pixels are mistakenly classified as part of the myocardium. Further investigation should be performed in order to limit these minority cases.  
When comparing the single- vs. multi-modality approach, it was found that the latter consistently outperformed the first. This supports the hypothesis that the information from Cine MRI is indeed helpful in improving the segmentation performance on DE MRI.  
Figure 10: Box-and-whisker plot of the Dice score distribution for the myocardium on myocardial infarction cases before and after applying data augmentation. The white circles correspond to the outliers.  
One of the limitations of the data augmentation method is the intensity pattern for synthetic MI generation. In particular, our current strategy does not take into account the generation of MI with persistent microvascular obstruction. Therefore, future work should be focused on investigating additional strategies that would allow us to create synthetic images that represent this particular condition in MI cases and therefore make the models more robust. Moreover, our pathology-specific data augmentation could be applied to the automatic segmentation of DE MRI concerning other diseases where the DE MRI is abnormal, such as myocarditis or hypertrophic cardiomyopathy.