TY - GEN
T1 - A Self-supervised 3D/2D Registration Method for Incomplete DSA Vessels
AU - Xu, Yizhou
AU - Meng, Cai
AU - Li, Yanggang
AU - Li, Ning
AU - Ren, Longfei
AU - Xia, Kun
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - For vascular interventional surgery, the preoperative 3D computed tomography (CT) has complete information of vessels but is not convenient for obvervation, while the intraoperative 2D digital subtraction angiography (DSA) is easy for doctors to monitor the vascular conditions real-timely but information is incomplete in each frame. As a result, 2D/3D registration, which is the technology to fuse information from images of different modal, is useful for the guidance of vascular interventional surgery. In this paper, we proposed a self-supervised 2D/3D vascular registration method to improve the performance on DSAs with incomplete vessels. The proposed method contains a rigid and an elastic registration stage, for regressing the 6-dim parameters to obtain a center image and fine-tuning respectively. In addition, a patch-based content loss is introduced to the rigid registration step to give an appropriate similarity measure for images with incomplete vessels, and a masked elastic module is introduced to simulate the incompletion and deformation caused by breath or heart beats on the real vessels in elastic registration. We evaluated our method on both simulated and real images. Experiments prove that our proposed method is effective to register CT and DSA images.
AB - For vascular interventional surgery, the preoperative 3D computed tomography (CT) has complete information of vessels but is not convenient for obvervation, while the intraoperative 2D digital subtraction angiography (DSA) is easy for doctors to monitor the vascular conditions real-timely but information is incomplete in each frame. As a result, 2D/3D registration, which is the technology to fuse information from images of different modal, is useful for the guidance of vascular interventional surgery. In this paper, we proposed a self-supervised 2D/3D vascular registration method to improve the performance on DSAs with incomplete vessels. The proposed method contains a rigid and an elastic registration stage, for regressing the 6-dim parameters to obtain a center image and fine-tuning respectively. In addition, a patch-based content loss is introduced to the rigid registration step to give an appropriate similarity measure for images with incomplete vessels, and a masked elastic module is introduced to simulate the incompletion and deformation caused by breath or heart beats on the real vessels in elastic registration. We evaluated our method on both simulated and real images. Experiments prove that our proposed method is effective to register CT and DSA images.
KW - 2D/3D registration
KW - Intervention surgery
KW - Self-supervised learning
KW - X-ray coronary angiography
UR - https://www.scopus.com/pages/publications/85148692638
U2 - 10.1007/978-3-031-25191-7_2
DO - 10.1007/978-3-031-25191-7_2
M3 - 会议稿件
AN - SCOPUS:85148692638
SN - 9783031251900
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 13
EP - 31
BT - Biomedical and Computational Biology - 2nd International Symposium, BECB 2022, Revised Selected Papers
A2 - Wen, Shiping
A2 - Yang, Cihui
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Symposium on Biomedical and Computational Biology, BECB 2022
Y2 - 13 August 2022 through 15 August 2022
ER -