## 4 Experiments and Results
### Results with the ACDC Dataset
#### 4.3.4 Final Result and Case Studies  
The final result is defined by joining the MYO, LVC, and RV masks generated by the proposed method. Table 6 presents the overall results of Dice or IoU obtained by the proposed method. This table shows the evaluation of the method applied to the same test set, whose slices are divided into two groups referring to the cardiac phases of end-diastole (ED) and end-systole (ES). This analysis is similar to that performed in the ACDC challenge.  
The results show that the proposed method produces better segmentations for ED slices. During this cardiac phase, the heart is more dilated, making the structures of interest more visible. In the case of ES slices, the results show a decrease, especially those related to the RV. In this cardiac phase, the structures are more compressed, visually changing their sizes and shapes, contributing to segmentation inaccuracies.  
Figure 7 shows the qualitative results of the proposed method for the basal, medial, and apical slices. In these examples, it is possible to verify the changes in the shape and size of the structures of interest throughout the volume. Despite this, the method generated good segmentation results, emphasizing Dice values above 95% for the basal slice case. In the medial example, it is possible to observe the segmentation of the three structures, both presenting expressive results. In this example, the method generated a segmentation for MYO slightly larger than the ground truth due to the texture similarity with very close regions. The same can be seen in the apical example in the case of the RV. However, although this structure is very small in this type of slice, the method produced a good result.  
Figure 8 shows some examples in which the method is applied in slices during the ES phase, which, as previously mentioned, is the one that presents segmentation inaccuracies more frequently. The basal example does not have structures of interest, but the method generated false positives due to the similarity between background regions with MYO and LVC. In the medial example, the three structures are visible. The MYO and LVC segmentations showed promising results. However, the RV has a more contracted and stretched shape, so the method only identified part of this structure. Finally, in the apical example, LVC and MYO are very small but maintain their shape features. On the other hand, the RV has a different shape and a texture similar to the background regions, which leads the method to produce false negatives.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Stages**} & \multicolumn{2}{c}{**Dice**} & **Dice** & **Dice** & **IoU** & **IoU** & **IoU** \\  & & **(Overall)** & **ED** & **ES** & **(Overall)** & **ED** & **ES** \\ \hline MYO and LVC segmentation & LVC & 0.9236 & 0.9473 & 0.9000 & 0.8851 & 0.9145 & 0.8557 \\ (EAIS-Net + U-Net) & MYO & 0.8547 & 0.8616 & 0.8479 & 0.7708 & 0.7703 & 0.7713 \\ \hline RV Segmentation & RV & 0.8213 & 0.8546 & 0.7880 & 0.7623 & 0.8003 & 0.7244 \\ (IRAX-Net) & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Approaches selected to compose the proposed method and the final result of the segmentation process.  
Lastly, regarding the computational complexity, the average execution time of the proposed method is 3.3 s per exam. Among the stages of the proposed method, the MYO and LVC segmentation has the highest average execution time, which is 1.69 s per exam. This stage is divided into several substages, leading to a longer processing time. Therefore, it is possible to infer that the proposed method achieves promising results in a time considered satisfactory.  
Figure 8: Qualitative segmentation results produced by the proposed method in ES slices.  
Figure 7: Qualitative results of the proposed method. Examples of basal, medial, and apical slices.