Layer Separation in X-ray Angiograms for Vessel Enhancement with Fully Convolutional Network

Percutaneous coronary intervention is a treatment for coronary artery disease, which is performed under image-guidance using X-ray angiography. The intensities in an X-ray image are a superimposition of 2D structures projected from 3D anatomical structures, which makes robust information processing challenging. The purpose of this work is to investigate to what extent vessel layer separation can be achieved with deep learning, especially adversarial networks. To this end, we develop and evaluate a deep learning based method for vessel layer separation. In particular, the method utilizes a fully convolutional network (FCN), which was trained by two different strategies: an L-1 loss and a combination of L-1 and adversarial losses. The experiment results show that the FCN trained with both losses can well enhance vessel structures by separating the vessel layer, while the L-1 loss results in better contrast. In contrast to traditional layer separation methods [1], both our methods can be executed much faster and thus have potential for real-time applications.