Abstract
Digital subtraction angiography (DSA) is a fluoroscopic technique used to clearly visualize blood vessels. However, accurate segmentation of coronary arteries cannot be directly obtained from DSA images because of motion artifacts. In this paper, a fully convolutional network is designed to segment the coronary arteries from DSA images instead of angiographic images. First, an ORPCA method with intra-frame and inter-frame constraints is introduced to enhance the vessel structure in DSA. Then, an enhanced DSA image-guided segmentation network, which is a fully convolutional network composed of an encoder path and a decoder path, is proposed to extract the coronary arteries to learn the vascular features from the enhanced vascular structures. The experimental results demonstrate that the proposed method is more effective and accurate in coronary artery segmentation, compared with state-of-the-art methods.
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Acknowledgement
This work was supported by the National Key R&D Program of China (2017YFC0107900), the China Postdoctoral Science Foundation (2015M580962), and the National Science Foundation Program of China (61672099, 61501030).
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Fan, J., Du, C., Song, S., Cong, W., Hao, A., Yang, J. (2019). Enhanced Subtraction Image Guided Convolutional Neural Network for Coronary Artery Segmentation. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_59
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DOI: https://doi.org/10.1007/978-981-13-9917-6_59
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