## 1 Introduction  
Cardiovascular diseases (CVDs) are the leading cause of death worldwide. The World Health Organization (WHO) estimates that 17.9 million people died from cardiovascular disease in 2019, accounting for 32% of deaths worldwide. Of these deaths, 85% were due to coronary heart disease and stroke [1]. Early diagnosis and appropriate treatment can significantly reduce mortality and improve the quality of life. The diagnostic process commonly consists of two main parts. The first part refers to obtaining images of cardiac structures with the help of imaging devices. Imaging techniques, such as computed tomography (CT) or magnetic resonance imaging (MRI), allow inspection of a human body without surgically cutting into it. CT has become one of the most common imaging techniques for examining the human cardiovascular system. The second part of the diagnostic process is interpreting and quantifying images using advanced image processing methods. Manual or semiautomatic segmentation of cardiovascular structures from CT images is a time-consuming process, often prone to error. Not only may segmentation drawn by two radiologists differ (inter-observer variability), but there will also not be an agreement between segmentation drawn by the same radiologist at different occasions (intra-observervariability). This is mainly due to the high noise in CT images and their fluctuating contrast. For example, the coronary sinus is almost devoid of contrast in the CT data, making it difficult to distinguish from surrounding tissues. The papillary muscles are complex and small, making them difficult to distinguish from the noise. Segmentation of the right ventricular wall is complex because of the thin myocardium and considerable inter-patient individual variability. Therefore, there is a need to develop automated tools to facilitate cardiovascular images segmentation and interpretation. Deep learning, particularly convolutional neural networks (CNNs), shows promising results in automatic segmentation for a variety of medical applications [2; 3]. A popular deep learning architecture in the field of semantic segmentation for biomedical applications is UNet [4]. UNet's symmetric, encoder-decoder architecture allows automatic learning of features at different levels. Nevertheless, low-level features learned in the encoder are rich with feature space information but lack semantic information, and high-level features learned from the decoder are the opposite. Thus, the direct concatenation of these features may not produce the most optimal results. Researchers have already offered plenty of improvement schemes based on UNet. For example, addition of residual connections in ResUNet architecture propagates information over layers, allowing building of deeper neural networks and reducing the impact of exploding or vanishing gradients, which ultimately alleviates training performance [5]. Moreover, contrary to UNet, architectures such as RefineNet [6], DenseNet [7], or SegNet [8] only use the highest layer features while they lack in low-level representation. This was further improved in DeepLabV3 [9] and ResUNet++ [10], where extracted features are passed through the Atrous Spatial Pyramid Pooling (ASPP) module to obtain multi-scale information [9]. Although it is advantageous to have as many extracted features as possible, not all features are equally important. Therefore, distinguishing between feature importance allows the network to focus on the most important features. To solve this problem, SENet architecture [11] introduces squeeze-and-excitation operations that can capture the importance degree of each feature channel through the feature recalibration strategy. Based on the importance degree, the less useful features are suppressed while more useful features are enhanced. Similarly, the attention mechanism dynamically allocates the input weights of neurons to selectively focus on the most critical part of the information [12] and are often introduced into UNet-based architectures to improve their performance.