## 4 Experiments and Results
### Results with the M&Ms Dataset
The medial case illustrates one of the main flaws observed in the T_ACDC experiment: the partial segmentation of structures, indicating the generation of false negatives. These failures occur less frequently in the T_MMS experiment, reiterating the importance of training the networks with examples from the M&Ms dataset. In the example in Figure 9, the Dice values went from 0.7830 (LVC), 0.9352 (MYO), and 0.8351 (RV) to 0.9237 (LVC), 0.8047 (MYO), and 0.9048 (RV). In the basal case, an additional drawback observed in the T_ACDC experiment is the generation of false positives. The basal slices consist of LVC and MYO regions, with no RV present. However, some slices may not contain any of the structures of interest but have regions similar in shape and texture, which leads to segmentation inaccuracies. This effect is reduced in the T_MMS experiment because the M&Ms dataset contains more slices with such characteristics than the ACDC dataset. Finally, a z-test was performed to evaluate the statistical difference between the results found for each experiment. In this test, the null hypothesis is assumed to be that there is a significant difference. The significance level (\(\alpha\)) used is 0.05, so if the \(p\)-value is less than \(\alpha\), the null hypothesis is accepted. Table 15 presents the \(p\)-values obtained from the tests. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Structure** & **Dice ED** & **Dice ES** & **IoU ED** & **IoU ES** \\ \hline \multirow{3}{*}{T\_ACDC} & LVC & 0.8385 & 0.7136 & 0.7707 & 0.5994 \\  & MYO & 0.6806 & 0.6707 & 0.5524 & 0.5535 \\  & RV & 0.7252 & 0.6639 & 0.6299 & 0.5505 \\ \hline \multirow{3}{*}{T\_MMS} & LVC & 0.8618 & 0.8059 & 0.7987 & 0.7119 \\  & MYO & 0.7330 & 0.7598 & 0.6076 & 0.6451 \\ \cline{1-1}  & RV & 0.7218 & 0.6949 & 0.6294 & 0.5839 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: M&Ms dataset: results of the proposed method separated by cardiac phase. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{_p_**-Values**} \\ \cline{2-5}  & **Dice** & **IoU** & **SENS** & **PREC** \\ \hline LVC & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\ MYO & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\ RV & 0.2247 & 0.1754 & 0.0836 & 0.0229 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: \(p\)-values found for the evaluation metrics of the proposed method in the T_ACDC and T_MMS experiments. The results indicate a significant difference in all metrics used to evaluate the segmentation of MYO and LVC, corroborating the observation that the proposed method also shows a promising performance when applied to the M&Ms dataset, especially in the experiment where the network models are trained with this dataset. In the case of RV, the difference is significant only in the accuracy results, reinforcing the finding that, in the T_MMS experiment, the method was able to reduce false positives. Comparison with Related Works  
In Table 16, the results obtained by the proposed method are compared with the related works in the context of the M&Ms dataset. The metric used for analysis is the Dice coefficient, as indicated by the challenge. It is emphasized that most of the methods presented report Dice separately by cardiac phase (ED and ES), but some use overall Dice. Therefore, to facilitate comparison, the overall Dice coefficient and the Dice coefficient per cardiac phase are presented in this table. [PERSON] et al. [35] used 2D U-Net with adversarial back-propagation training to handle inter-exam variability due to acquisition by different scanners. The input slices used in the mentioned work were previously cropped in the dimensions \(192\times 192\), with the cardiac