## 4 Experiments and Results
### Results with the M&Ms Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**LVC**} & \multicolumn{3}{c}{**MYO**} & \multicolumn{3}{c}{**RV**} \\ \hline
**Method** & **O** & **ED** & **ES** & **O** & **ED** & **ES** & **O** & **ED** & **ES** \\ \hline [PERSON] et al. [35] & - & 0.905 & 0.848 & - & 0.772 & 0.820 & - & 0.876 & 0.809 \\ [PERSON] et al. [39] & - & 0.896 & 0.772 & - & 0.761 & 0.721 & - & 0.820 & 0.698 \\ [PERSON] et al. [37] & - & 0.939 & 0.886 & - & 0.839 & 0.867 & - & 0.910 & 0.860 \\ [PERSON] et al. [36] & - & 0.930 & 0.894 & - & 0.764 & 0.828 & - & 0.883 & 0.822 \\ [PERSON] et al. [40] & - & 0.924 & 0.889 & - & 0.827 & 0.859 & - & 0.878 & 0.843 \\ [PERSON] et al. [41] & 0.945 & - & - & 0.869 & - & - & 0.912 & - & - \\ [PERSON] et al. [38] & 0.925 & - & - & 0.821 & - & - & 0.901 & - & - \\ [PERSON] et al. [42] & - & 0.940 & 0.890 & - & 0.839 & 0.870 & - & 0.909 & 0.856 \\
**Proposed Method** & **0.833** & **0.861** & **0.805** & **0.746** & **0.733** & **0.759** & **0.708** & **0.721** & **0.694** \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Overall (O) and per cardiac phase (ED and ES) Dice results of the proposed method and related works for LVC, MYO, and RV segmentation. Figure 9: Qualitative results of the proposed method in the M&Ms dataset experiments, with (**A**) the input, (**B**) the ground truth, (**C**) the result of the T_ACDC experiment, and (**D**) the result of the T_MMS experiment. structures properly centered, characterizing a semi-supervised scenario. In contrast, the proposed method simulates a fully automated scenario, with ROI extraction involving the structures of interest, also considering the possible displacements within the ROIs. [PERSON] et al. [39] used two networks: a so-called ST network, aimed at preprocessing the slices, which are resized to 256 \(\times\) 256, and a 2D U-Net, which receives the output of the ST network to produce the final segmentation. On the other hand, the proposed method applies as preprocessing-only techniques aimed at reducing the noise of cine-MRI, and the multi-stage segmentation avoids the resizing of the input slices in order not to change their features, especially in the ES phase, when the cardiac structures are more compressed. Thus, the proposed method obtained superior results in the cases of MYO and LVC in ES slices. The works that outperform the proposed method are characterized by using techniques that demand a higher computational cost. [PERSON] et al. [37], for example, used nnU-Net, a framework which allows automated network training from hyperparameter estimation and search for the best set of data augmentation operations. [PERSON] et al. [40] and [PERSON] et al. [41] proposed methods that receive the 3D volume of the exam at the input, requiring more robust hardware. The proposed method, in turn, was developed through experiments with the ACDC dataset, which has a similar context to M&Ms. However, the latter contains more heterogeneity due to the different types of scanners used in image acquisition. Nevertheless, it is observed that the proposed method obtained relevant results for the evaluation metrics, surpassing some works that were developed considering, since its conception, the aspects of the M&Ms dataset.