# Attention V-Net: A Modified V-Net Architecture for Left Atrial Segmentation
## 1 Introduction  
Cardiovascular diseases have become an important factor affecting human life and health [1; 2]. In recent years, cardiac interventional therapy, as an advanced diagnosis and treatment method between internal and surgical procedures, has been widely used to cure cardiovascular diseases [3; 4]. Among them, atrial septal puncture location surgery is the key to the success of interventional treatment of cardiovascular disease. How to accurately locate the puncture point to quickly puncture the atrial septum is the key to successful surgery. However, due to the lack of accurate and reliable 3D imaging feedback, atrial septal puncture location is still a challenging process, which requires doctors to have a great deal of experience. Left atrial segmentation is of great significance for doctors to quickly and accurately locate the atrial septal puncture position, and to better complete interventional surgery. In recent years, algorithms based on deep learning technology, especially convolutional neural networks (CNNs), have made great breakthroughs in left atrial image processing tasks. [PERSON] et al. [5] first introduced CNN into medical image segmentation, using a sliding window to fetch the local area around the pixel to train the network. However, this strategy only uses high-level features, and does not make full use of features with more marginal information. Furthermore, it is very slow because of the great quantity of training data. Later, [PERSON] et al. [6] proposed FCN (Full convolutional network), for image semantic segmentation, extending the classification from image level to pixellevel. However, these methods did not fully consider the relationship between pixels, and ignored the spatial regularization steps used in the common segmentation methods, resulting in a lack of spatial consistency. Therefore, the results obtained are not detailed enough. Then, [PERSON] et al. [7] proposed U-Net, which has both a contraction path that captures context information and a symmetric expansion path that allows precise positioning. Meanwhile, it can be trained based on FCN with a small number of images end-to end. Despite the popularity of previous approaches, they usually can only process 2D images. Unfortunately, most clinically applied cardiology data consists of 3D volumes. Therefore, [PERSON] et al. [8] recently proposed V-Net, an FCN based on 3D images. The dataset used by the author is made up of 3D medical images, which is different from the common 2D data. Operations such as convolution in the V-Net network structure also use 3D processing mode, in which a residual function inspired by [9] is also learned, which ensures convergence in less training time and obtains good segmentation accuracy. In addition, in view of the scarcity of labeled left atrial image data, many methods were proposed in recent years to develop high-performance left atrial segmentation models to reduce labeled data. Among them, the semi-supervised learning framework has achieved many successful results, which can directly learn from limited labeled data and a large amount of unlabeled data to obtain high quality segmentation results. These methods can be roughly divided into two categories: regularization based on data perturbation or model perturbation [10; 11] and consistency constraints based on multi-task level [12; 13]. Most of them take V-Net as the backbone network of the algorithm, and the skip connection structure of V-Net model improves the shortcomings of FCN, such as not considering global context information and insufficient segmentation. However, the hierarchical convolution structure in a V-Net encoder-decoder neglects the local region features of the segmentation target to some extent, which may lead to the misclassification of the target and other objects. The attention mechanism method can enable the network to focus on the local region of the feature map.