## 4 Experiments and Results
### Results with the M&Ms Dataset  
This section presents the results obtained by the proposed method in the tests with the M&Ms dataset. It is important to highlight that these experiments were performed once the method had been consolidated. Therefore, this section focuses solely on the final results obtained by applying the selected techniques in each stage of the proposed method. Two experiments were performed. In the first, called T_ACDC, the proposed method is used in a transfer learning approach, in which the network models are trained with the ACDC dataset, and the test is done with the M&Ms dataset. In the second one, called T_MMS, the M&Ms dataset is used for both training the models and testing. It is important to note that in the latter experiment, the networks were trained with the same hyperparameters presented in Section 4.3. The same data augmentation techniques were applied to the training set, which grew from 3286 slices to 24,616 slices. The validation and test sets have 806 and 3218 slices, respectively. In all cases, the input slices were preprocessed as they were done in the experiments with the aforementioned ACDC dataset. Table 13 shows the comparative results obtained. The evaluation metrics achieved more expressive values in the T_MMS experiment than in T_ACDC. Thus, it is possible to verify that the training of the models with images from the M&MS dataset yielded better results when compared to the use of models previously trained with the ACDC dataset. The M&Ms dataset has greater diversity among the tests due to the different scanner models used in the acquisition. Therefore, training the models with the M&Ms dataset can introduce features to the networks that are not present in the ACDC dataset. With the LVC and MYO structures, all metrics showed a noticeable increase. Taking the Dice coefficient as an example, in the T_ACDC experiment, the proposed method obtained 0.7761 and 0.6757 for LVC and MYO, respectively. In the T_MMS experiment, these values increased to 0.8339 and 0.7464. For the RV, better performance of the method is also observed in the T_MMS experiment. However, the values of the metrics do not show a significant increase. For example, Dice and IoU went from 0.6945 and 0.5902, respectively, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment** & **Structure** & **Dice** & **IoU** & **SENS** & **PREC** \\ \hline \multirow{3}{*}{T\_ACDC} & LVC & 0.7761 & 0.6851 & 0.7445 & 0.7139 \\  & MYO & 0.6757 & 0.5529 & 0.6529 & 0.6105 \\  & RV & 0.6945 & 0.5902 & 0.7712 & 0.7432 \\ \hline \multirow{3}{*}{**T\_MMS**} & LVC & 0.8339 & 0.7553 & 0.8620 & 0.8215 \\  & MYO & 0.7464 & 0.6263 & 0.7546 & 0.7679 \\ \cline{1-1}  & RV & 0.7083 & 0.6067 & 0.7890 & 0.7675 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Results of the experiments performed with the M&Ms dataset. to 0.7083 and 0.6067, representing an increase of approximately 0.1. The most significant increase can be observed in the PREC metric, which increased from 0.7432 to 0.7675. These results indicate a reduction in the production of false positives in RV segmentation in the T_MMS experiment. Table 14 shows the results divided between the ED and ES cardiac phases. In the T_ACDC experiment, it can be seen that the best results were obtained for the slices in the ED phase, in which the structures are more visible than in the ES phase. In the T_MMS experiment, only the cases of LVC and RV have better results in the ED phase. For MYO, the method performs better for slices in ES. This result is influenced by the more considerable amount of examples that the M&Ms dataset has, allowing the networks to learn more features about this type of slice. In Figure 9, the results of the segmentation produced by the proposed method for each experiment performed can be seen. In the apical example, the Dice values obtained were from 0.8726 (LVC), 0.7498 (MYO), and 0.8023 (RV) in the T_ACDC experiment, to 0.9478 (LVC), 0.8579 (MYO), and 0.8472 (RV) in the T_MMS experiment.