## 4 Experiments and Results
### Results with the ACDC Dataset
#### 4.3.5 Segmentation Impact Analysis  
Some experiments were carried out to evaluate the impact of the segmentation of cardiac structures by changes made in the proposed method. The first experiment evaluates the individual performance of the networks used in the proposed method (U-Net, EAIS-Net, and IRAX-Net). In this case, these networks represent the single-stage segmentation process. This result, therefore, is compared with that obtained by the proposed multi-stage method. This experiment intends to demonstrate the rationale for choosing a multi-stage approach over a single-stage one. A comparative analysis can be seen in Table 7.  
In U-Net, EAIS-Net, and IRAX-Net tests, the input slice dimensions were resized to \(160\times 160\) due to hardware limitations. In general, the results achieved by these approaches are inferior to the proposed method. One of the observed causes is that resizing the slices reduces the size of the structures of interest. This reduction complicates the feature extraction process, especially in the apical regions of the exam, where the structures of interest are naturally small. On the other hand, the proposed method divides the segmentation process into stages, allowing better handling of class imbalance and keeping the size of cardiac structures unchanged.  
The other experiment aimed to analyze the impact of the ROI extraction stage on the final segmentation result. They replace the automatically extracted ROIs with a test set containing the same exams but composed of ROIs extracted from the ground truth, thus simulating a semi-supervised process. The results are presented in Table 8.  
Through this experiment, it is possible to notice that the automatic process causes some losses for LVC and MYO. On the other hand, the results related to the RV obtained a slight advantage compared to the semi-supervised process. Thus, the Z-test statistical test [57] was performed to evaluate whether the difference between the results is statistically significant. The parameters used were the sample size (382 slices) and the significance level (\(\alpha\)) of 0.05 (recommended). The null hypothesis is that there is a significant difference between the results. In Table 9, the results of the hypothesis test are shown. The values found, called P-values, are all greater than \(\alpha\). Therefore, the conclusion is that there is no significant difference between the results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structure** & **Methods** & **Dice** & **IoU** \\ \hline \multirow{4}{*}{LVC} & U-Net & 0.8592 & 0.7837 \\  & EAIS-Net & 0.8840 & 0.7784 \\  & IRAX-Net & 0.8000 & 0.7147 \\  & **Proposed Method** & **0.9236** & **0.8851** \\ \hline \multirow{4}{*}{MYO} & U-Net & 0.4457 & 0.3213 \\  & EAIS-Net & 0.4399 & 0.3177 \\  & IRAX-Net & 0.6169 & 0.4715 \\  & **Proposed Method** & **0.8547** & **0.7708** \\ \hline \multirow{4}{*}{RV} & U-Net & 0.5975 & 0.4995 \\  & EAIS-Net & 0.6309 & 0.5353 \\ \cline{1-1}  & IRAX-Net & 0.5307 & 0.4187 \\ \cline{1-1}  & **Proposed Method** & **0.8213** & **0.7623** \\ \hline \end{tabular}
\end{table}
Table 7: Comparison between the results obtained by the proposed method and other single-stage segmentation approaches.  
Based on this analysis, it is observed that the ROI extraction stage presents a low impact, confirming the feasibility of the automatic segmentation proposal in the application scenario, in which a computational solution is important to minimize the efforts of specialists.