## 5 Experiments and Results
### Quantitative Results  
In our experiments, we trained four encoder-decoder-based architectures: (1) the original UNet [4], (2) ResUNet [5], (3) ResUNet++ [10], and (4) the proposed AB-ResUNet+. These experiments provide insight into our proposed architecture's competitiveness compared to previously published architectures relevant to our work. The detailed qualitative segmentation results are shown in Tables 3-6. According to results, we can observe that addition of attention blocks in the skip connection obtains finer results than original UNet. Generally, after the skip connection, there are two features that are combined--one is from the decoder layer and the others from the matching encoder layer. In the original UNet, these two features are directly combined with a concatenation function. On the other hand, in our proposed AB-ResUNet+ model, we combine these features using attention block. Therefore, after the channel compression, the encoded feature contains more local information from the input sample and global \begin{table}
\begin{tabular}{l l l l} \hline
**Data Augmentation Method** & **Values** & **Training Parameters** & **Value** \\ \hline Random crop & Crop: 128 \(\times\) 128 & Initial learning rate & 0.004 \\  & Randomness = 50\% & & \\ Horizontal flip & Randomness = 50\% & Number of epoch & 200 \\ Vertical flip & Randomness = 50\% & Filter size & \(3\times 3\) \\ Scale augmentation & Scaling factor: [1, 1.3] & Pooling size & \(2\times 2\) \\ Random rotation & Angle: Random [\(-\)20 to +20] & Batch size & 4 \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of data augmentation methods and training parameters for UNet, ResUNet, ResUNet++, and AB-ResUNet+ model training. information of the channel, which yields better results. The ASPP block assists the decoded feature to possess more semantic information of the input sample while the interaction between encoding and decoding features may form a group of feature maps with both sample global information and semantic information. Addition of residual connections mostly accelerates networks' learning process. Moreover, we may observe that UNet can segment the general outlines of most cardiovascular structures from the results. However, it cannot segment regions with high contrast variability and prominent edges. ResUNet has a better performance than UNet, in which the residual connection enhances the segmentation ability. We can see from the results that the proposed AB-ResUNet+ provides higher segmentation accuracy than the other original UNet, ResUnet, and ResUNet++ regarding DSC and mIoU for all cardiac structures, except for PVW, where ResUNet++ achieves the highest DSC. Furthermore, regarding precision and recall, we observe that the proposed AB-ResUNet+ outperforms in all cardiovascular structures except RVW, LAW, and IVC, respectively. This is probably due to the overfitting that occurs for these structures. Nevertheless, the proposed architecture significantly outperformed the baseline architectures for all other datasets. The ROC curve showing the tradeoff between sensitivity and specificity of our proposed AB-ResUNet+ network architecture is shown in Figure 4. The area under the ROC curve (AUC) for CS, DA, IVC, LAA, LAW, PM, PML, PAA, PA, RVW, and SVC were 0.74, 0.96, 0.91, 0.90, 0.81, 0.75, 0.72, 0.94, 0.93, 0.90, and 0.95, respectively. From the ROC curve, we can see that DA, PA, and SVC obtain a significant altitude, which is reflected in a very robust segmentation performance.