TY - GEN
T1 - ME-GraphSAGE
T2 - 18th Chinese Conference on Image and Graphics Technology and Application Conference, IGTA 2023
AU - Ding, Yang
AU - Fu, Tianyu
AU - Chen, Sigeng
AU - Xiao, Deqiang
AU - Fan, Jingfan
AU - Song, Hong
AU - Yu, Yang
AU - Yang, Jian
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - Automatic labeling of coronary artery segments improves efficiency in the diagnosis and treatment of coronary artery disease, but faces challenges due to the class imbalance between main and side branches. State-of-the-art methods primarily focus on position-direction and pixel features, which leads to suboptimal performance when dealing with bifurcated segments. In this paper, we propose a minority class feature enhanced GraphSAGE (ME-GraphSAGE), which alleviates class imbalance by generating minority class nodes. We extract bifurcation features from Digital Subtraction Angiography (DSA) images taken from four commonly observed views of coronary arteries. These features, along with other relevant ones, are fed into ME-GraphSAGE to enhance the accuracy of segment labeling in bifurcated regions. By combining the results from the four views, a higher-level sixteen-segment-based coronary labeling is obtained. Our method is evaluated on a dataset of 205 coronary DSA sequences. The experimental results show that ME-GraphSAGE significantly outperforms state-of-the-art methods in labeling coronary artery branches.
AB - Automatic labeling of coronary artery segments improves efficiency in the diagnosis and treatment of coronary artery disease, but faces challenges due to the class imbalance between main and side branches. State-of-the-art methods primarily focus on position-direction and pixel features, which leads to suboptimal performance when dealing with bifurcated segments. In this paper, we propose a minority class feature enhanced GraphSAGE (ME-GraphSAGE), which alleviates class imbalance by generating minority class nodes. We extract bifurcation features from Digital Subtraction Angiography (DSA) images taken from four commonly observed views of coronary arteries. These features, along with other relevant ones, are fed into ME-GraphSAGE to enhance the accuracy of segment labeling in bifurcated regions. By combining the results from the four views, a higher-level sixteen-segment-based coronary labeling is obtained. Our method is evaluated on a dataset of 205 coronary DSA sequences. The experimental results show that ME-GraphSAGE significantly outperforms state-of-the-art methods in labeling coronary artery branches.
KW - DSA
KW - bifurcation features
KW - coronary artery labeling
KW - minority class feature enhanced GraphSAGE
UR - http://www.scopus.com/inward/record.url?scp=85176010785&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-7549-5_32
DO - 10.1007/978-981-99-7549-5_32
M3 - Conference contribution
AN - SCOPUS:85176010785
SN - 9789819975488
T3 - Communications in Computer and Information Science
SP - 440
EP - 455
BT - Image and Graphics Technologies and Applications - 18th Chinese Conference, IGTA 2023, Revised Selected Papers
A2 - Yongtian, Wang
A2 - Lifang, Wu
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 17 August 2023 through 19 August 2023
ER -