Segmenting anatomical structures in the chest is a crucial step in many automatic disease detection applications. Multi-atlas based methods are developed for this task, however, due to the required deformable registration step, they are often computationally expensive and create a bottle neck in terms of processing time. In contrast, convolutional neural networks (CNNs) with 2D or 3D kernels, although slow to train, are very fast in the deployment stage and have been employed to solve segmentation tasks in medical imaging. A recent improvement in performance of neural networks in medical image segmentation was recently reported when dice similarity coefficient (DSC) was used to optimize the weights in a fully convolutional architecture called V-Net. However, in the previous work, only the DSC calculated for one foreground object is optimized, as a result the DSC based segmentation CNNs are only able to perform a binary segmentation. In this paper, we extend the V-Net binary architecture to a multi-label segmentation network and use it for segmenting multiple anatomical structures in cardiac CTA. The method uses multi-label V-Net optimized by the sum over DSC for all the anatomies, followed by a post-processing method to refine the segmented surface. Our method takes averagely less than 3 sec to segment a full CTA volume. In contrast, the fastest multi-atlas based methods published so far take around 10 mins. Our method achieves an average DSC of 76% for 16 segmented anatomies using four-fold cross validation, which is close to the state-of-the-art.