## 4 Experiments and Results
### Results with the ACDC Dataset
#### 4.3.6 Comparison with Related Works
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Dice** & **Dice** & **HD** & **HD** & **Vol.** & **Vol.** & **Mass** & **Mass** \\  & **ED** & **ES** & **ED** & **ES** & **ES C.** & **ES B.** & **ED C.** & **ED B.** \\ \hline
**Proposed Method** & **0.880** & **0.892** & **13.440** & **12.590** & **0.972** & **3.210** & **0.971** & \(-\)**1.870** \\ [PERSON] et al. [26] & 0.904 & 0.923 & 7.014 & 7.328 & 0.988 & \(-\)1.984 & 0.987 & \(-\)2.547 \\ [PERSON] and [PERSON] [33] & 0.891 & 0.904 & 8.264 & 9.575 & 0.983 & \(-\)2.134 & 0.992 & \(-\)2.904 \\ [PERSON] et al. [44] & 0.894 & 0.905 & 7.906 & 9.912 & 0.980 & \(-\)1.100 & 0.988 & \(-\)1.820 \\ [PERSON] and [PERSON] [31] & 0.873 & 0.895 & 8.197 & 8.318 & 0.988 & \(-\)1.79 & 0.989 & \(-\)2.100 \\ [PERSON] et al. [58] & 0.886 & 0.902 & 9.586 & 9.291 & 0.980 & 1.160 & 0.986 & \(-\)1.827 \\ [PERSON] et al. [59] & 0.881 & 0.897 & 8.651 & 9.598 & 0.979 & 0.296 & 0.987 & \(-\)2.906 \\ [PERSON] et al. [30] & 0.889 & 0.898 & 9.841 & 12.582 & 0.979 & \(-\)2.572 & 0.990 & \(-\)2.873 \\ [PERSON] et al. [62] & 0.882 & 0.897 & 9.757 & 11.256 & 0.986 & \(-\)4.464 & 0.989 & \(-\)11.586 \\ [PERSON] et al. [27] & 0.892 & 0.901 & 8.703 & 10.637 & 0.983 & \(-\)9.602 & 0.982 & \(-\)6.861 \\ [PERSON] et al. [29] & 0.884 & 0.896 & 8.708 & 9.264 & 0.960 & \(-\)7.804 & 0.984 & \(-\)12.405 \\ [PERSON] et al. [60] & 0.875 & 0.894 & 11.121 & 10.687 & 0.971 & 0.906 & 0.963 & \(-\)0.960 \\ [PERSON] et al. [32] & 0.891 & 0.901 & 10.575 & 13.891 & 0.934 & 1.590 & 0.986 & 2.977 \\ [PERSON] et al. [42] & 0.905 & 0.923 & 7.389 & 7.373 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 11: MYO segmentation. The evaluation was performed on the ACDC online platform. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Dice** & **Dice** & **HD** & **HD** & **EF** & **EF** & **Vol. ED** & **Vol. ED** \\  & **ED** & **ES** & **ED** & **ES** & **C.** & **B.** & **C.** & **B.** \\ \hline
**Proposed Method** & **0.910** & **0.860** & **16.580** & **17.300** & **0.746** & **2.190** & **0.944** & **8.940** \\ [PERSON] et al. [26] & 0.951 & 0.904 & 8.205 & 11.655 & 0.910 & \(-\)3.750 & 0.992 & 0.900 \\ [PERSON] and [PERSON] [31] & 0.936 & 0.884 & 10.183 & 12.234 & 0.899 & \(-\)2.118 & 0.989 & 3.550 \\ [PERSON] and [PERSON] [33] & 0.936 & 0.889 & 13.289 & 14.367 & 0.894 & \(-\)1.292 & 0.990 & 0.906 \\ [PERSON] et al. [44] & 0.900 & 0.860 & 14.660 & 17.560 & 0.743 & 1.810 & 0.931 & 7.370 \\ [PERSON] et al. [58] & 0.934 & 0.885 & 11.052 & 12.650 & 0.869 & \(-\)0.872 & 0.986 & 2.372 \\ [PERSON] et al. [29] & 0.941 & 0.882 & 10.318 & 14.053 & 0.872 & \(-\)2.228 & 0.991 & \(-\)3.722 \\ [PERSON] et al. [59] & 0.933 & 0.884 & 13.718 & 13.323 & 0.865 & \(-\)0.874 & 0.986 & 2.078 \\ [PERSON] et al. [30] & 0.935 & 0.879 & 13.994 & 13.930 & 0.858 & \(-\)2.246 & 0.982 & \(-\)2.896 \\ [PERSON] et al. [27] & 0.932 & 0.883 & 12.670 & 14.691 & 0.851 & 1.218 & 0.977 & \(-\)2.290 \\ [PERSON] et al. [60] & 0.928 & 0.872 & 11.879 & 13.399 & 0.852 & \(-\)4.610 & 0.980 & 3.596 \\ [PERSON] et al. [61] & 0.916 & 0.845 & 14.049 & 15.926 & 0.781 & \(-\)0.662 & 0.983 & 7.340 \\ [PERSON] et al. [32] & 0.929 & 0.886 & 14.189 & 16.042 & 0.863 & 0.670 & 0.973 & 4.000 \\ [PERSON] et al. [42] & 0.950 & 0.895 & 8.513 & 12.167 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 12: RV segmentation. The evaluation was performed on the ACDC online platform. Regarding MYO segmentation, the proposed method presents a better Dice ED than AdaEn-Net and the approach developed by [PERSON] et al. [60]. Both approaches use a single CNN to segment the three structures simultaneously. The input slices are in their original size. The proposed method, in turn, is divided into stages, the first being responsible for extracting an ROI, thus eliminating more background regions and helping to obtain better results.