## 5 Experiments and Results
### Quantitative Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**AB-ResUNet+**} \\ \cline{2-5}  & **DSC** & **mIoU** & **Recall** & **Precision** \\ \hline CS & 0.7168 \(\pm\) 0.0204 & 0.6856 \(\pm\) 0.0242 & 0.7022 \(\pm\) 0.0302 & 0.7475 \(\pm\) 0.0262 \\ DA & 0.9345 \(\pm\) 0.0184 & 0.9244 \(\pm\) 0.0184 & 0.9024 \(\pm\) 0.0242 & 0.9544 \(\pm\) 0.0128 \\ IVC & 0.9145 \(\pm\) 0.0246 & 0.9012 \(\pm\) 0.0164 & 0.9218 \(\pm\) 0.0186 & 0.9124 \(\pm\) 0.0361 \\ LAA & 0.8822 \(\pm\) 0.0186 & 0.7422 \(\pm\) 0.0216 & 0.7624 \(\pm\) 0.0214 & 0.7826 \(\pm\) 0.0214 \\ LAW & 0.8512 \(\pm\) 0.0248 & 0.7512 \(\pm\) 0.0146 & 0.7422 \(\pm\) 0.0218 & 0.7846 \(\pm\) 0.0154 \\ PM & 0.7744 \(\pm\) 0.0194 & 0.7822 \(\pm\) 0.0084 & 0.7831 \(\pm\) 0.0242 & 0.7842 \(\pm\) 0.0342 \\ PML & 0.5864 \(\pm\) 0.0320 & 0.5222 \(\pm\) 0.0124 & 0.5478 \(\pm\) 0.0188 & 0.5744 \(\pm\) 0.0212 \\ PAA & 0.9412 \(\pm\) 0.0145 & 0.8828 \(\pm\) 0.0145 & 0.8845 \(\pm\) 0.0086 & 0.8842 \(\pm\) 0.0308 \\ PA & 0.9425 \(\pm\) 0.0142 & 0.9244 \(\pm\) 0.0246 & 0.9266 \(\pm\) 0.0262 & 0.9284 \(\pm\) 0.0180 \\ RVW & 0.8722 \(\pm\) 0.0120 & 0.6844 \(\pm\) 0.0312 & 0.6848 \(\pm\) 0.0212 & 0.8684 \(\pm\) 0.0145 \\ SVC & 0.9544 \(\pm\) 0.0088 & 0.8466 \(\pm\) 0.0248 & 0.8424 \(\pm\) 0.0312 & 0.8842 \(\pm\) 0.0168 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Obtained segmentation results of different cardiovascular structures for AB-ResUNet+ network architecture. Figure 4: The ROC curve and AUC values for CS, DA, IVC, LAA, LAW, PM, PML, PAA, PA, RVW, and SVC dataset of our proposed segmentation method with AB-ResUNet+ network architecture. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**ResUNet++**} \\ \cline{2-5}  & **DSC** & **mIoU** & **Recall** & **Precision** \\ \hline CS & 0.6688 \(\pm\) 0.0251 & 0.5122 \(\pm\) 0.0245 & 0.6924 \(\pm\) 0.0223 & 0.7276 \(\pm\) 0.0124 \\ DA & 0.9022 \(\pm\) 0.0344 & 0.9042 \(\pm\) 0.0212 & 0.9144 \(\pm\) 0.0128 & 0.9432 \(\pm\) 0.0098 \\ IVC & 0.8852 \(\pm\) 0.0382 & 0.8812 \(\pm\) 0.0198 & 0.9128 \(\pm\) 0.0242 & 0.9314 \(\pm\) 0.0124 \\ LAA & 0.8512 \(\pm\) 0.0344 & 0.7144 \(\pm\) 0.0174 & 0.7466 \(\pm\) 0.0262 & 0.7622 \(\pm\) 0.0272 \\ LAW & 0.8412 \(\pm\) 0.0342 & 0.7244 \(\pm\) 0.0245 & 0.7789 \(\pm\) 0.0098 & 0.7645 \(\pm\) 0.0212 \\ PM & 0.7434 \(\pm\) 0.0248 & 0.7524 \(\pm\) 0.0301 & 0.7614 \(\pm\) 0.0146 & 0.7622 \(\pm\) 0.0246 \\ PML & 0.6104 \(\pm\) 0.0218 & 0.3945 \(\pm\) 0.0271 & 0.5424 \(\pm\) 0.0178 & 0.5948 \(\pm\) 0.0407 \\ PAA & 0.9242 \(\pm\) 0.0212 & 0.8644 \(\pm\) 0.0245 & 0.8685 \(\pm\) 0.0268 & 0.8948 \(\pm\) 0.0108 \\ PA & 0.9142 \(\pm\) 0.0266 & 0.9012 \(\pm\) 0.0168 & 0.9116 \(\pm\) 0.0234 & 0.9214 \(\pm\) 0.0124 \\ RVW & 0.9114 \(\pm\) 0.0248 & 0.7644 \(\pm\) 0.0198 & 0.8416 \(\pm\) 0.0247 & 0.8498 \(\pm\) 0.0342 \\ SVC & 0.9245 \(\pm\) 0.0302 & 0.8012 \(\pm\) 0.0242 & 0.8418 \(\pm\) 0.0164 & 0.8216 \(\pm\) 0.0284 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Obtained segmentation results of different cardiovascular structures for ResUNet++ network architecture. Comparison with State-of-the-Art Methods  
The proposed approach was compared with other similar deep learning approaches in terms of image segmentation accuracy, as shown in Table 7. The most similar work, regarding observed structures, to our work is work by [PERSON] et al. [31]. They used UNet architecture for PAA, DA, SVC, IVC, PA, CS, RVW, and LAW segmentation. Nevertheless, their dataset consisted of 206 patients with 2D images of size \(512\times 512\), which contributed to high accuracy. Moreover, they used CAT images. The use of contrast and ECG gating that may have better delineated the border between the vessel wall and lumen may be partly attributable to higher DSC regarding PAA and DA segmentation. Another approach introduced by [PERSON] et al. [44] uses fully convolutional neural networks (FCNs) with three-dimensional (3D) conditional random fields (CRFs) for LAA segmentation.