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 L1 loss and a combination of L1 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 L1 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.

Additional Metadata
Persistent URL dx.doi.org/10.1007/978-3-030-01364-6_5, hdl.handle.net/1765/111855
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Citation
Hao, H. (Haidong), Ma, H, & van Walsum, T.W. (2018). Layer separation in X-ray angiograms for vessel enhancement with fully convolutional network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). doi:10.1007/978-3-030-01364-6_5