Attention V-Net: A Modified V-Net Architecture for Left Atrial Segmentation

Abstract

We propose a fully convolutional neural network based on the attention mechanism for 3D medical image segmentation tasks. It can adaptively learn to highlight the salient features of images that are useful for image segmentation tasks. Some prior methods enhance accuracy using multi-scale feature fusion or dilated convolution, which is basically artificial and lacks the flexibility of the model itself. Therefore, some works proposed the 2D attention gate module, but these works process 2D medical slice images, ignoring the correlation between 3D image sequences. In contrast, the 3D attention gate can comprehensively use the information of three dimensions of medical images. In this paper, we propose the Attention V-Net architecture, which uses the 3D attention gate module, and applied it to the left atrium segmentation framework based on semi-supervised learning. The proposed method is evaluated on the dataset of the 2018 left atrial challenge. The experimental results show that the Attention V-Net obtains improved performance under evaluation indicators, such as Dice, Jaccard, ASD (Average surface distance), and 95HD (Hausdorff distance). The result indicates that the model in this paper can effectively improve the accuracy of left atrial segmentation, therefore laying the foundation for subsequent work such as in atrial reconstruction. Meanwhile, our model is of great significance for assisting doctors in treating cardiovascular diseases.