## 6 Discussion  
In this work, we aimed to develop a new deep learning model for accurate segmentation of most of the great vessels, left atrial and right ventricular walls, and coronary sinus. So far, we have presented our newly developed AB-ResUNet+ architecture that utilizes residual learning, squeeze and excitation operations, Atrous Spatial Pyramid Pooling (ASPP), and the attention mechanism for accurate and effective segmentation of complex cardiovascular structures. The encoder consists of squeeze-and-excitation and residual blocks. The output of the residual blocks in the encoder part is routed through the squeeze-and-excitation block to increase the representational power of the network. Squeeze and excitation operations capture the importance degree of each feature channel through feature recalibration strategy. Based on the importance degree, the less useful channel features are suppressed while useful features are enhanced. The decoder consists of residual blocks and generates final segmentation predictions. Moreover, the main improvement is mainly achieved by adding the channel attention block into the skip connection. The addition of the channel attention block in each skip connection improves the coding ability in each layer and successfully eliminates irrelevant and redundant information. This improves the network's ability to distinguish between feature importance and focus on the most important features. The ASPP block is placed at the bottom of the network and acts as a bridge between the encoder and the decoder, increasing the field of view of the filters and allowing them to include a wider context. To evaluate the quality of our design choice, we implemented and trained a total of four networks, namely, UNet, ResUNet, ResUNet++, and the proposed AB-ResUNet+, and evaluated them on eleven test datasets of complex cardiovascular structures, namely, coronary sinus (CS), descending aorta (DA), inferior vena cava (IVC), left atrial appendage (LAA), left atrial wall (LAW), papillary muscle (PM), posterior mitral leaflet (PML), proximal ascending aorta (PAA), pulmonary aorta (PA), right ventricular wall (RVW), and superior vena cava (SVC). The proposed network achieved more accurate DSC results for most of the datasets used compared to ResUNet++. In particular, our proposed architecture improved the DSC of CS, DA, IVC, LAA, LAW, PM, PAA, PA, and SVC by 4.57%, 2.76%, 2.63%, 3.11%, 0.95%, 3.15%, 1.68%, 2.37%, and 2.68%, respectively. However, for the PML and RVW datasets, we obtained the worst DSC compared to ResUNet++. Moreover, we obtained better results for mIoU compared to ResUNet++, except for the LAA and PAA datasets. Moreover, we observed that the proposed AB -ResUNet+ architecture achieved higher DSC and mIoU metrics, as well as competitive precision and recall, for most datasets compared to the baseline models. Based on the obtained results, it is clear that the inclusion of AB blocks in the proposed AB-ResUNet+ architecture leads to slightly better results than the plain ResUNet++ architecture. The designed AB block in skip connections helped  
Figure 8: An example of original images from LVW, RVW, PM, DA, IVC, and CS dataset with an overlay of successful segmentation prediction (red) and corresponding GT (green). the proposed network to exploit the intra-slice information to a certain extent; thus, the network obtained higher segmentation results. There are several limitations associated with our current study. First, while obtained results are promising, it is important to determine whether such a model may be applied in clinical practice. Given that clinical practice often involves recordings of live video, a model with a fast inference time is required to process images in real time. Therefore, a proposed method should be improved by using lightweight models. Second, although the proposed method improves performance, the lack of medical data related to observed images limits the segmentation effect.