## 5 Experiments and Results
### Qualitative Results  
Figure 5 shows a visual comparison of the successful segmentation predictions from the test datasets, generated by UNet, ResUNet, ResUNet++, and the proposed AB-ResUNet+, respectively. The most accurate segmentation results are obtained for DA, IVC, PAA, and PA. This is probably due to their circular structure and the high contrast in the images around these structures.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Authors**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Modality**} & \multicolumn{8}{c}{**Cardiac Structures**} \\ \cline{3-13}  & & & **CS** & **DA** & **IVC** & **LAA** & **LAW** & **PML** & **PAA** & **PA** & **RVW** & **SVC** \\ \hline [PERSON] et al. [31] & UNet & CTA & 0.720 & 0.953 & 0.903 & ✗ & 0.625 & ✗ & 0.969 & 0.775 & 0.685 & 0.937 \\ [PERSON] et al. [44] & FCN + CRFs & CTA & ✗ & ✗ & 0.9476 & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\ [PERSON] et al. [44] & FCN + CRFs & CT & ✗ & 0.88 & ✗ & ✗ & ✗ & 0.83 & ✗ & ✗ & ✗ \\ [PERSON] et al. [46] & Bayesian VoxDRN & MRI & ✗ & ✗ & ✗ & ✗ & ✗ & 0.857 & ✗ & ✗ \\ Proposed & AI-ResUNet+ & CT & 0.72 & 0.93 & 0.91 & 0.88 & 0.85 & 0.77 & 0.59 & 0.94 & 0.94 & 0.87 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of DSC results obtained with our proposed AB-ResUNet+ architecture and the state-of-the-art segmentation methods.  
Figure 5: Qualitative results comparison of the different cardiac structures. Rows from left to the right represent original image, ground truth, UNet results, ResUNet results, ResUNet++ results, and AB-ResUNet+, respectively.  
Nevertheless, while observing obtained segmentation predictions, we found some cases of missing or wrong segmentation results. A missing segmentation refers to an incomplete segmentation, i.e., when part of the area to be segmented is missing. This is a common problem in small structures such as CS, PML, and SVC. In particular, since the mask of PML is represented almost as a single point, the model could not segment it completely in case of any segmentation failure. An example of incorrect segmentation of CS and SVC can be found in Figure 6.  
The overfitting issue is successfully overcome in most cases. Nevertheless, we observe some overfitting while segmenting PM and LAA, where the model hardly distinguishes between background and these structures due to low contrast and anatomical complexity. An example of such errors can be found in Figure 7.  
Visual comparisons between the original image, the manual labeling, and the AB-ResUNet+ model prediction are shown in Figure 8. The segmentation prediction examples and GT overlays over the original image for LVW, RVW, PM, DA, IVC, and CS more accurately illustrate the potential difficulties in segmentation due to low image quality, high and low contrast differences, and the highly anatomical complexity of the structures. The difficulty in identifying the irregularly shaped CS, especially with suboptimal contrast fluoroscopy, may account for the lower accuracy compared to the other vessels. The complex structure of LVWs and RVWs, which varies in different layers and in different patients, makes them a particular challenge for automatic segmentation methods.  
Figure 6: An example of incorrect segmentation results for CS and SVC datasets. From left to right: original image, ground truth, UNet results, ResUNet results, ResUNet++ results, and AB-ResUNet+.  
Figure 7: An examples of overfitting issue for PM and LAA datasets. From left to right: original image, ground truth, UNet results, ResUNet results, ResUNet++ results, and AB-ResUNet+.