## 4 Discussion
### Analysis of the Cine to Tagged Image Transformation Models  
While seemingly producing inconsistent contrast and despite not deforming tag lines through acquisition time, the physics-driven model implemented in this work appears to give an advantage when used to pre-train a myocardium segmentation model. Future research could evaluate the trade-off between the time and effort needed to reach higher levels of sophistication in the augmentation technique versus the added segmentation performance, specifically with regard to the propagation of motion of the tag lines in time.  
The style transfer models developed in this work highlight the important potential of domain transformation models in biomedical imaging tasks. Previously used to augment datasets by transforming images taken by one vendor or research center to another [21], CycleGAN is also able to transfer domain-specific patterns from vastly different imaging modalities. The flexibility of this implementation enables further research to create more cardiac MRI-specific data augmentation models. For example, the simulation of respiratory motion artifacts or poor ECG gating. Additionally, resource- and time-intensive annotations can be shared across modalities thanks to the cycle-consistency constraint. Indeed, future work could include the LV and RV classes during model training as they are commonly available in public datasets. Cycle-consistent style transfer does bring some disadvantages, however. First, this type of model is difficult to evaluate quantitatively due to the generative nature of the task. Above that, it is prone to hallucinations, which can present an important problem in certain biomedical imaging applications. The model transforming a low-SNR domain into a high-SNR one usually presents vastly worse performance. In this work, for example, the tagged-to-_cine_ model \(G_{B\to A}\) shows many hallucinations and is unable to produce physiologically consistent-appearing structures. Finally, the CycleGAN used in  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Split** \\ \(\gamma\) \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Train** \\ **Avg.** \\ \end{tabular} } & \multicolumn{2}{c}{**DSC (\(\uparrow\))**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} & \multirow{2}{*}{
\begin{tabular}{c} **Rank** \\ **Std.** \\ \end{tabular} } \\  & & **Std.** & & **Avg.** & & **Std.** & & **Avg.** & & **Std.** & \\ \hline
0.000 & 0.880 & 0.025 & 0.768 & 0.094 & 3.082 & 0.668 & 7.701 & 8.967 & 7 \\ \hline
0.001 & 0.815 & 0.025 & 0.771 & 0.045 & 3.375 & 0.771 & **5.358** & 5.946 & 2 \\
0.005 & 0.886 & 0.023 & 0.770 & 0.102 & 2.976 & 0.658 & 7.628 & 9.414 & 4 \\
0.050 & 0.872 & 0.027 & **0.801** & 0.065 & 3.301 & 0.746 & 5.616 & 1.989 & 1 \\
0.100 & 0.865 & 0.026 & 0.798 & 0.083 & 3.547 & 0.849 & 6.423 & 5.314 & 3 \\
0.500 & 0.789 & 0.023 & 0.736 & 0.050 & 3.496 & 0.958 & 6.449 & 10.825 & 6 \\
1.000 & 0.585 & 0.035 & 0.556 & 0.041 & 4.838 & 3.672 & 5.715 & 1.975 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance metrics for the shape distance loss weight factor \(\gamma\). Displayed are the Dice coefficient (DSC) and 95 th percentile Hausdorff Distance (HD-95) for which higher and lower are better, respectively. Each model is a ResNetVAE architecture trained from scratch on tagged CMR images for 200 epochs. The average (Avg.) and standard deviation (Std.) statistics are computed over the training and testing dataset splits containing 1000 and 250 2D SAX-tagged MR images, respectively. The rank is based on the cumulative ranking for both evaluation metrics.  
this work is set with common baseline parameters for training. Future work could look into possible performance improvements using a more fine-grained tuning strategy for these parameters.