## 2 Related Research
### Previous Methods for Cardiovascular Segmentation  
Recent advances in medical imaging have been facilitated by the widespread application of deep learning techniques. Two-stage segmentation methods consisting of localization and segmentation steps [13; 14; 15], FCNs with deep supervision [16; 17], multi-view CNNs [18; 19], and residual network variants are most commonly used for various cardiovascular segmentation tasks. Few works use UNet architecture to provide experimental analysis observing the influence of different parameters for final segmentation results. For example, [PERSON] et al. [20] investigate two-dimensional (2D) UNet and three-dimensional (3D) UNet with various hyperparameters. [PERSON] et al. [21] evaluated a 2D and 3D UNet trained with varying Dice and cross-entropy losses. [PERSON] et al. [22] implemented an M-Net architecture in which the decoding layer's feature maps are concatenated with those of the previous layer. [PERSON] et al. [23] propose a method based on UNet combined with image sequence information. They introduce two modules: the contextual extraction module and the segmentation module. The context extraction module can fully extract the context features of the image to be segmented and effectively combine the sequence features. The segmentation module is an encoder-decoder module and the input image can directly predict a segmented image. The module effectively learns the characteristics of the original image and avoids feature loss and gradient dispersion by the design of the skip connection. [PERSON] et al. [24] implemented an ensemble of 2D and 3D UNet architectures (with residual connections along with the upsampling layers). [PERSON] et al. [25] implemented a 3D UNet with residual connections instead of a commonly used concatenation operator. [PERSON] et al. [26] proposed TransUNet architecture, with inherent global self-attention mechanisms into UNet. In the encoder, the transformer tokenizes image patches from a CNN feature map. At the same time, the decoder upsamples the encoded features before combining them with the high-resolution CNN feature maps to enable exact localization. Transformers overcome UNet's limited localization ability due to insufficient low-level details. [PERSON] et al. [27] propose UNet-based architecture named swin-Unet. They use hierarchical swin transformer with shifted windows [28] as the encoder to extract context features, and symmetric swin transformer-based decoder with patch expanding layer designed to perform the upsampling operation to restore the spatial resolution of the feature maps.  
Furthermore, structure-wise, commonly observed are the whole heart and its main substructures, such as left ventricle (LV), right ventricle (RV), left atrium (LA), right atrium (RA), pulmonary artery (PA), descending aorta (DA), and coronary arteries [29; 30]. However, the coronary sinus (CS), right ventricular wall (RVW), left atrial wall (LAW), superior vena cava, and inferior vena cava (SVC, IVC) are somewhat less explored structures. This is mainly due to the lack of annotated datasets. Recently, [PERSON] et al. [31] used the UNet architecture to segment the proximal ascending and descending aorta (PAA, DA), superior and inferior vena cava (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW), and left atrial wall (LAW) and made the dataset publicly available. This dataset is used in our work.