Abstract
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.
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References
Ma, H.: Automatic online layer separation for vessel enhancement in X-ray angiograms for percutaneous coronary interventions. Med. Image Anal. 39, 145–161 (2017)
Ma, H., et al.: Layer separation for vessel enhancement in interventional X-ray angiograms using morphological filtering and robust PCA. In: Linte, C.A., Yaniv, Z., Fallavollita, P. (eds.) AE-CAI 2015. LNCS, vol. 9365, pp. 104–113. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24601-7_11
Litjens, G.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Hao, H., et al.: Vessel layer separation in X-ray angiograms with fully convolutional network. In: Proceedings of SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Nie, D., et al.: Medical image synthesis with context-aware generative adversarial networks. In: Descoteaux, M., et al. (eds.) MICCAI 2017, Part III. LNCS, vol. 10435, pp. 417–425. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_48
Wolterink, J.M.: Generative adversarial networks for noise reduction in low-dose CT. IEEE TMI 36(12), 2536–2545 (2017)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Isola, P., et al.: Image-to-image translation with conditional adversarial networks. In: CVPR (2017)
Wang, Z.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Kingma, D., Ba, J.: ADAM: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
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Hao, H., Ma, H., van Walsum, T. (2018). Layer Separation in X-ray Angiograms for Vessel Enhancement with Fully Convolutional Network. In: Stoyanov, D., et al. Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. LABELS CVII STENT 2018 2018 2018. Lecture Notes in Computer Science(), vol 11043. Springer, Cham. https://doi.org/10.1007/978-3-030-01364-6_5
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DOI: https://doi.org/10.1007/978-3-030-01364-6_5
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