J Shanghai Jiaotong Univ Sci ›› 2025, Vol. 30 ›› Issue (3): 472-481.doi: 10.1007/s12204-023-2653-4
收稿日期:
2023-03-14
接受日期:
2023-04-28
出版日期:
2025-06-06
发布日期:
2025-06-06
付泽宇1,付 庄1,管贻生2
Received:
2023-03-14
Accepted:
2023-04-28
Online:
2025-06-06
Published:
2025-06-06
摘要: 在血管介入手术中引入路径规划和视觉导航可以为医生提供直观的参考和指导。本研究基于X射线冠状动脉造影图像血管骨架提取和狭窄病变诊断的预处理结果,先利用聚类判断交叉点的连通性,再通过改进的Dijkstra算法自动规划手术路径。在此基础上,引入中间点对路径进行分段修正,提高系统精度。最后通过外极约束逆投影变换重建冠脉三维模型,并标注出最优路径,实现多角度三维视觉导航。临床数据实验结果表明,与使用传统Dijkstra算法相比,改进方法能够减少中间点需求,提升计算效率,且与人工标定路径的平均误差降低到整体优化前的4%。三维重建和重投影的结果进一步定性和定量地验证了整体方案的可靠性。
中图分类号:
. 血管介入手术路径规划及三维视觉导航[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 472-481.
Fu Zeyu, Fu Zhuang, Guan Yisheng. Vascular Interventional Surgery Path Planning and 3D Visual Navigation[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 472-481.
[1] The Writing Committee of the Report on Cardiovascular Health and Diseases in China. Report on cardiovascular health and diseases in China 2021: An updated summary [J]. Chinese Circulation Journal, 2022, 37(6): 553-578 (in Chinese). [2] LEVY R I, JESSE M J, MOCK M B. Position on percutaneous transluminal coronary angioplasty (PTCA) [J]. Circulation, 1979, 59(3): 613. [3] LOWE H C, OESTERLE S N, KHACHIGIAN L M. Coronary in-stent restenosis: Current status and future strategies [J]. Journal of the American College of Cardiology, 2002, 39(2): 183-193. [4] KIRIŞLI H A, SCHAAP M, METZ C T, et al. Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography [J]. Medical Image Analysis, 2013, 17(8): 859-876. [5] LESSARD S, LAU C, CHAV R, et al. Guidewire tracking during endovascular neurosurgery [J]. Medical Engineering & Physics, 2010, 32(8): 813-821. [6] GAO M K, CHEN Y M, ZHANG D H, et al. Path planning of vascular access surgery based on improved ant colony algorithm [J]. Journal of Shanghai University (Natural Science Edition), 2019, 25(2): 198-205 (in Chinese). [7] AZIZI A, TREMBLAY C, MARTEL S. Trajectory planning for vascular navigation from 3D angiography images and vessel centerline data [C]//2017 International Conference on Manipulation, Automation and Robotics at Small Scales. Montreal: IEEE, 2017: 1-6. [8] GUO J, SUN Y, GUO S X. A training system for vascular interventional surgeons based on local path planning [C]//2021 IEEE International Conference on Mechatronics and Automation. Takamatsu: IEEE, 2021: 1328-1333. [9] FU Z Y, FU Z, LU C Z, et al. Robust implementation of foreground extraction and vessel segmentation for X-ray coronary angiography image sequence [DB/OL]. (2022-09-15). https://arxiv.org/abs/2209.07237 [10] ZHUANG Y, CHEN G B, FU Z. Segmentation and diagnosis of angiocardiography image [J]. Machinery & Electronics, 2018, 36(4): 16-19, 37 (in Chinese). [11] SUI C X, FU Z, FU Z Y, et al. A novel method for vessel segmentation and automatic diagnosis of vascular stenosis [C]//2019 IEEE International Conference on Robotics and Biomimetics. Dali: IEEE, 2019: 918-923. [12] CHERKASSKY B V, GOLDBERG A V, RADZIK T. Shortest paths algorithms: Theory and experimental evaluation [J]. Mathematical Programming, 1996, 73(2): 129-174. [13] FANG H H, ZHU J J, AI D N, et al. Greedy soft matching for vascular tracking of coronary angiographic image sequences [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(5): 1466-1480. [14] ÇIMEN S, GOOYA A, GRASS M, et al. Reconstruction of coronary arteries from X-ray angiography: A review [J]. Medical Image Analysis, 2016, 32: 46-68. [15] BANERJEE A, GALASSI F, ZACUR E, et al. Point-cloud method for automated 3D coronary tree reconstruction from multiple non-simultaneous angiographic projections [J]. IEEE Transactions on Medical Imaging, 2019, 39(4): 1278-1290. [16] JIA Y S, XIAO D Q, YAN Q, et al. A method for reconstructing 3D skeleton of coronary artery from 2D X-ray angiographic images [C]//IMIP 2022: 2022 4th International Conference on Intelligent Medicine and Image Processing. Tianjin: ACM, 2022: 70-75. |
[1] | . 基于改进FCOS算法的钢丝绳芯输送带损伤X射线图像检测[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(2): 309-318. |
[2] | . 基于双流自编码器的无监督动作识别[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(2): 330-336. |
[3] | . 基于空间特征学习与多粒度特征融合的行人重识别[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(2): 363-374. |
[4] | 周苏, 钟泽滨. 基于车载智能手机的实时车辆及行人测距[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(6): 1081-1090. |
[5] | 鄢丛强1,2, 郭正玉3,4, 蔡云泽 1,2. 基于改进CycleGAN的SAR图像舰船尾迹数据增强[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(4): 702-711. |
[6] | LONARE Savita1,2, BHRAMARAMBA Ravi2. 基于图卷积网络的联邦式隐私保护交通预测方法[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 509-517. |
[7] | 吕峰,王新彦,李磊,江泉,易政洋. 基于嵌入式YOLO轻量级网络的树木检测算法[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 518-527. |
[8] | 宋立博a,费燕琼b. 新型Lite YOLOv4-Tiny算法及其在裂纹智能检测中的应用[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 528-536. |
[9] | 沈傲1, 2,胡冀苏2, 3,金鹏飞4,周志勇2,钱旭升2, 3,郑毅2,包婕4,王希明4,戴亚康1, 2. 基于课程学习训练的聚合注意力网络Multi-SEANet用于MRI图像的格里森级别组无创预测[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(1): 109-119. |
[10] | 薛永波a,刘 钊b,李泽阳a,朱 平a. 基于改进分水岭算法和U-net神经网络模型的复合材料CT图像分割方法[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(6): 783-792. |
[11] | . 基于锥型体素建模和单目相机的鸟瞰图语义分割和体素语义分割[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(1): 100-113. |
[12] | . 行人轨迹预测的动作感知编码器–解码器网络[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(1): 20-27. |
[13] | SONG Hao-hao (宋好好), LU Zhen (陆 臻). Image Fusion Scheme Based on Nonsubsampled Contourlet and Block-Based Cosine Transform[J]. J Shanghai Jiaotong Univ Sci, 2012, 17(1): 8-012. |
阅读次数 | ||||||||||||||||||||||||||||||||||||||||||||||
全文 9
|
|
|||||||||||||||||||||||||||||||||||||||||||||
摘要 86
|
|
|||||||||||||||||||||||||||||||||||||||||||||