## 4 Experiments and Results
### Results with the ACDC Dataset
#### 4.3.1 ROI Extraction Stage  
The experiments in this stage examined two U-Nets, A and B. Only medial slices of MYO were used to train U-Net A. On the other hand, U-Net B was trained to segment this structure over all slices of the exam. As mentioned in Section 3.1, MYO segmentation is the basis for delimiting bounding boxes in the ROI extraction step. So, a comparison study was conducted to determine which method is superior for creating the reference segmentations. Since U-Net A represents a specialized training approach, only medial slices were selected to compose the network's training set. However, this medial slices set is small. So, to increase its size, the following data augmentation operations were used: horizontal flip, rotation in the range \([-5 circ,5 circ]\), and vertical and horizontal translation. The size increased from 120 to 1080 slices.  
As the U-Net B was trained with all the slices (including basal and apical), it was unnecessary to use data augmentation. The training set, in this case, contains 1154 slices.  
U-Nets A and B have about 18 million parameters to be tuned. They were trained using the following hyperparameters: 300 epochs, Jaccard Loss function, learning rate of 0.001, Adam optimizer [50], decay factor of 0.1, and the early stopping technique [51].  
As stated in Section 3.1, in this stage, the models were trained to segment only the MYO in the medial slices due to its better visibility, thus serving as a reference for ROI extraction. So, the test set used for the evaluation contains only medial slices, totaling 100 cases. Table 1 shows the outcomes of the MYO segmentation as a component of the ROI extraction stage.  
This evaluation indicates that the specialized training represented by U-Net A is a better approach than U-Net B. The first achieved the best results for the test set with Dice of 0.9368 and IoU of 0.8820. In contrast, the latter produced less satisfactory results, thus showing the impact of having been trained with a greater variety of slices.  
An additional analysis was conducted using the mean absolute error (MAE) [52] and mean average precision (mAP) [53] metrics. These metrics quantify the similarity between manually extracted bounding boxes from the ground truth and automatically generated bounding boxes based on the reference segmentations produced by U-Nets A and B. The results are presented in Table 2. The IoU threshold used in the mAP calculation is \(IoU\geq 0.80\) in this evaluation. This value was selected to ensure higher confidence in the detections. Thus, the generated bounding boxes are classified as true positives only if the IoU calculated with the ground truth is equal to or greater than the defined threshold.  
The ROI extraction approach that used the masks generated by U-Net A as the segmentation reference obtained mAP of 0.90 and MAE of 2.93, which indicates that the extracted ROIs are very similar to the ground truth. MAE analysis indicates consistent results due to the low standard deviation. Furthermore, it is worth noting that in some cases, the MAE value is zero, indicating that the ROIs automatically extracted by U-Net A are identical to those obtained from the ground truth. Despite the input slices being resized to 160 \(\times\) 160, which could lead to some features being changed and relevant information lost, the results show that this resizing did not cause adverse effects on the generation of bounding boxes.  
In this evaluation, U-Net B also obtained the least expressive results, strengthening the choice of U-Net A to compose the ROI extraction stage in the proposed method. The high similarity with the ground truth indicates that all structures of interest (MYO, LVC, and RV) are kept within the ROIs, which will be used as input for the further stages of the proposed method.