@inproceedings{dda18dfede78476aa11fe23a04a29888,
title = "Enhanced Subtraction Image Guided Convolutional Neural Network for Coronary Artery Segmentation",
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.",
keywords = "Convolutional neural network, Coronary artery, Segmentation, Subtraction angiography",
author = "Jingfan Fan and Chenbin Du and Shuang Song and Weijian Cong and Aimin Hao and Jian Yang",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Singapore Pte Ltd.; 14th Conference on Image and Graphics Technologies and Applications, IGTA 2019 ; Conference date: 19-04-2019 Through 20-04-2019",
year = "2019",
doi = "10.1007/978-981-13-9917-6\_59",
language = "英语",
isbn = "9789811399169",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "625--632",
editor = "Yongtian Wang and Qingmin Huang and Yuxin Peng",
booktitle = "Image and Graphics Technologies and Applications - 14th Conference on Image and Graphics Technologies and Applications, IGTA 2019, Revised Selected Papers",
address = "德国",
}