Accurate segmentation of pulmonary vein (PV) and left atrium (LA) is essential for the preoperative evaluation and planning of total anomalous pulmonary venous connection (TAPVC), which is a rare but mortal congenital heart disease of children. However, manual segmentation is time-consuming and insipid. To free radiologists from the repetitive work, we propose an automatic deep learning method to segment PV and LA from Low-Dose CT images. In the method, attention mechanism is incorporated into the widely used V-Net and a novel grouped attention module is applied to enforce the segmentation performance of the V-Net. We evaluate our method on 68 3D Low-Dose CT images scanned from patients with TAPVC. The experiment result shows that our method outperforms the popular 3D-UNet and V-Net, with mean dice similarity coefficient (DSC) of 0.795 and 0.834 for the PV and LA respectively.Clinical relevance-We proposed a CNNs-based method for the automatic segmentation of PV and LA with good accuracy, which can be used for the preoperative evaluation and planning of TAPVC. Our method can improve the efficiency and reduce the workloads of radiologists (400 milliseconds vs. 2-3 hours per-case).