13 March 2018 Vessel layer separation in x-ray angiograms with fully convolutional network
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Abstract
Percutaneous coronary intervention is a minimally-invasive procedure to treat coronary artery disease. In such procedures, X-ray angiography, a real-time imaging technique, is commonly used for image guidance to identify lesion sites and navigate catheters and guide-wires within coronary arteries. Due to the physical nature of X-ray imaging, photon energy undergoes absorption when penetrating tissues, rendering a 2D projection image of a 3D scene, in which semi-transparent structures overlap with each other. The overlapping structures make robust information processing of X-ray images challenging. To tackle this issue, layer separation techniques for X-ray images were proposed to separate those structures into different image layers based on structure appearance or motion pattern. These techniques have been proven effective for vessel enhancement in X-ray angiograms. However, layer separation approaches still suffer either from spurious structures or non-real-time processing, which prevent their application in clinics. Purpose of this work is to investigate whether vessel layer separation from X-ray angiography images is possible via a data-driven strategy. To this end, we develop and evaluate a deep learning based method to extract the vessel layer. More specifically, U-Net, a fully convolutional network architecture, was trained to separate the vessel layer from the background. The results of our experiments show good vessel layer separation on 42 clinical sequences. Compared to the previous state-of-the-art, our proposed method has similar performance but runs much faster, which makes it a potential real-time clinical application.
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Haidong Hao, Haidong Hao, Hua Ma, Hua Ma, Theo van Walsum, Theo van Walsum, } "Vessel layer separation in x-ray angiograms with fully convolutional network", Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 105761V (13 March 2018); doi: 10.1117/12.2293561; https://doi.org/10.1117/12.2293561
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