TY - GEN
T1 - Enhanced Subtraction Image Guided Convolutional Neural Network for Coronary Artery Segmentation
AU - Fan, Jingfan
AU - Du, Chenbin
AU - Song, Shuang
AU - Cong, Weijian
AU - Hao, Aimin
AU - Yang, Jian
N1 - Publisher Copyright:
© 2019, Springer Nature Singapore Pte Ltd.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Convolutional neural network
KW - Coronary artery
KW - Segmentation
KW - Subtraction angiography
UR - http://www.scopus.com/inward/record.url?scp=85073895728&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-9917-6_59
DO - 10.1007/978-981-13-9917-6_59
M3 - Conference contribution
AN - SCOPUS:85073895728
SN - 9789811399169
T3 - Communications in Computer and Information Science
SP - 625
EP - 632
BT - Image and Graphics Technologies and Applications - 14th Conference on Image and Graphics Technologies and Applications, IGTA 2019, Revised Selected Papers
A2 - Wang, Yongtian
A2 - Huang, Qingmin
A2 - Peng, Yuxin
PB - Springer Verlag
T2 - 14th Conference on Image and Graphics Technologies and Applications, IGTA 2019
Y2 - 19 April 2019 through 20 April 2019
ER -