Jianjun Zhu 朱建軍

Jianjun Zhu 朱建軍

Assistant Professor working on medical imaging, AI, and surgical robotics

Research

My research integrates artificial intelligence, medical imaging, and robotic systems for image-guided diagnosis and minimally invasive intervention. It spans three connected themes:

  • Medical Image Computing: learning-based segmentation, registration, anatomical modeling, and treatment planning from CT, MRI, and X-ray data.
  • Computer-Assisted Intervention: tracking, multimodal registration, navigation, and real-time image guidance for minimally invasive procedures.
  • Interventional Surgical Robotics: vision and force perception, shared control, safety-aware assistance, and experimental validation of intelligent robotic systems.

Publications

Note: Unless otherwise indicated, the graphical abstracts on this page are AI-generated visual summaries based on the corresponding publications. They are provided for a quick overview and may not exactly reproduce the original figures or findings. If any information conflicts with the original paper, please refer directly to the paper.

Selected

  • J. Zhu, L. Zhu, T. Han, Y. Zhang, P. Chen, C. Li, Y. Huang, T. Zhang, D. Zhang, Z. Cai, C. Wang, A. Song, and G.-J. Teng, “Bending-Aware Vision Co-Pilot for Intelligent Robotic Assistance in Endovascular Intervention,” IEEE Transactions on Robotics, vol. 42, pp. 2990–3008, 2026. [Paper]

    A vision-based Co-Pilot combines clinician commands with bending-risk-aware assistance for endovascular robot control, with validation in robotic phantom and in-vivo experiments.

    Graphical abstract of a vision Co-Pilot that blends clinician intent with bending-risk-aware assistive regulation to command a modular endovascular robot, validated in phantom and in-vivo trials

  • P. Lyu, J. Zhang, L. Zhang, W. Liu*, C. Wang, and J. Zhu*, “Metaunetr: Rethinking token mixer encoding for efficient multi-organ segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2024, pp. 446–455.[Paper][Code]

    An efficient 3D segmentation architecture rethinks token mixing to balance multi-organ accuracy, computational cost, and practical deployment.

    Graphical abstract of MetaUNETR, from 3D abdominal CT through TriCruci token mixing and an efficient encoder to multi-organ segmentation

  • J. Xiong, P. Lyu, T. Lin, K. Song, C. Wang, and J. Zhu*, “A highly efficient segmentation method for abdominal multi-organs on laptop,” in MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation, Springer, 2024, pp. 116–131. [Paper] [Code]

    A lightweight two-phase pipeline enables accurate abdominal multi-organ segmentation under the memory and speed constraints of an ordinary laptop.

    Graphical abstract of the two-phase lightweight abdominal multi-organ segmentation pipeline designed for laptop inference

  • J. Zhu, C. Wang, Y. Zhang, M. Zhan, W. Zhao, S. Teng, L. Lu*, and G.-J. Teng*, “3D/2D Vessel Registration based on Monte Carlo Tree Search and Manifold Regularization,” IEEE Transactions on Medical Imaging, vol. 43, no. 5, pp. 1727–1739, 2023. [Paper]

    Monte Carlo tree search and manifold regularization align preoperative 3D vessels with intraoperative 2D angiography across rigid and non-rigid deformation.

    Graphical abstract of 3D and 2D vessel registration using Monte Carlo tree search for rigid alignment and manifold-regularized deformation

  • J. Zhu, C. Wang, S. Teng, J. Lu, P. Lyu, P. Zhang, J. Xu, L. Lu*, and G.-J. Teng*, “Embedding expertise knowledge into inverse treatment planning for low-dose-rate brachytherapy of hepatic malignancies,” Medical Physics, vol. 51, no. 1, pp. 348–362, 2024. [Paper]

    Clinical expertise is embedded into inverse optimization to produce efficient, patient-specific seed placement and dose plans for hepatic brachytherapy.

    Graphical abstract of expertise-guided brachytherapy planning, from multi-organ CT segmentation and knowledge retrieval to seed initialization and inverse dose planning

  • J. Zhu, H. Li, D. Ai*, Q. Yang, J. Fan, Y. Huang, H. Song, Y. Han, and J. Yang*, “Iterative closest graph matching for non-rigid 3d/2d coronary arteries registration,” Computer Methods and Programs in Biomedicine, vol. 199, p. 105 901, 2021. [Paper]

    Iterative graph correspondence and deformation estimation register 3D coronary anatomy to 2D angiograms for image-guided cardiovascular intervention.

    Graphical abstract of iterative closest graph matching for non-rigid 3D and 2D coronary artery registration

Journal Articles

  • J. Zhu, L. Zhu, T. Han, Y. Zhang, P. Chen, C. Li, Y. Huang, T. Zhang, D. Zhang, Z. Cai, C. Wang, A. Song, and G.-J. Teng, “Bending-Aware Vision Co-Pilot for Intelligent Robotic Assistance in Endovascular Intervention,” IEEE Transactions on Robotics, vol. 42, pp. 2990–3008, 2026. [Paper]

  • Y. Liu, Y. Wang, J. Xiao, X. He, C. Wang, J. Zhu, P. Lv, H. Cai, L. Qiu, Y. Zhu, Y. Li, and L. Lu, “Computed Tomography and Ultrasound-guided Robotic Assistance in Percutaneous Puncture in Abdominal Phantom and Porcine Liver Models,” IEEE Transactions on Medical Robotics and Bionics, vol. 7, no. 2, pp. 542–549, 2025. [Paper]

    Graphical abstract of CT and ultrasound fusion guidance for robotic abdominal puncture in phantom and porcine liver models

  • Y. Li, N. Fan, X. He, J. Zhu, J. Zhang*, and L. Lu*, “Research Progress in Predicting Hepatocellular Carcinoma with Portal Vein Tumour Thrombus in the Era of Artificial Intelligence,” Journal of Hepatocellular Carcinoma, pp. 1429–1438, 2024. [Paper]

    Graphical abstract of AI-assisted HCC with PVTT risk prediction using imaging, clinical data, radiomics, machine learning, and deep learning

  • P. Lyu, W. Liu*, T. Lin, J. Zhang, Y. Liu, C. Wang, and J. Zhu, “Semi-supervised Segmentation of Abdominal Organs and Liver Tumor: Uncertainty Rectified Curriculum Labeling Meets X-fuse,” Machine Learning: Science and Technology, vol. 5, no. 2, p. 025 047, 2024. [Paper] [Code]

    Graphical abstract of X-Fuse semi-supervised abdominal organ and liver tumor segmentation with spatial-frequency encoding and uncertainty rectification

  • C. Wang, L. Guo, J. Zhu, L. Zhu, C. Li, H. Zhu, A. Song, L. Lu, G.-J. Teng*, N. Navab, and Z. Jiang, “Review of robotic systems for thoracoabdominal puncture interventional surgery,” APL bioengineering, vol. 8, no. 2, 2024. [Paper]

    Graphical abstract reviewing registration, navigation, puncture guidance, respiratory compensation, and motion control in thoracoabdominal puncture robots

  • X. Wang, J. Zhu, Y. Wang, C. Wang, P. Chen, P. Lyu, J. Xu, and G.-J. Teng*, “A respiratory signal monitoring method based on dual-pathway deep learning networks in image-guided robotic-assisted intervention system,” The International Journal of Medical Robotics and Computer Assisted Surgery, vol. 20, no. 6, e70017, 2024. [Paper]

    Graphical abstract of dual-pathway respiratory monitoring using structured-light stereo images, point clouds, ConvLSTM, and PointLSTM

  • T. Zhang*, A. Gao, J. Zhu, B. Zhang, J. Lin, and Y. Ni, “A vascular shape reconstruction method based on multicore fbg sensing,” IEEE Sensors Journal, vol. 24, no. 3, pp. 2769–2778, 2024. [Paper]

    Graphical abstract of multicore FBG sensing, curvature and torsion estimation, and helical reconstruction of a three-dimensional vessel shape

  • J. Zhu, C. Wang, S. Teng, J. Lu, P. Lyu, P. Zhang, J. Xu, L. Lu*, and G.-J. Teng*, “Embedding expertise knowledge into inverse treatment planning for low-dose-rate brachytherapy of hepatic malignancies,” Medical Physics, vol. 51, no. 1, pp. 348–362, 2024. [Paper]

  • J. Zhu, C. Wang, Y. Zhang, M. Zhan, W. Zhao, S. Teng, L. Lu*, and G.-J. Teng*, “3D/2D Vessel Registration based on Monte Carlo Tree Search and Manifold Regularization,” IEEE Transactions on Medical Imaging, vol. 43, no. 5, pp. 1727–1739, 2023. [Paper]

  • G. Zhang, H.-C. Wong*, J. Zhu*, T. An, and C. Wang, “Jigsaw training-based background reverse attention transformer network for guidewire segmentation,” International Journal of Computer Assisted Radiology and Surgery, vol. 18, no. 4, pp. 653–661, 2023. [Paper]

    Graphical abstract of jigsaw training, RPCA background modeling, and reverse-attention transformer segmentation for single and dual guidewires

  • S. Jiang, S. Teng, J. Lu, C. Wang, T. Wen, J. Zhu, and G.-J. Teng*, “Pixeltopois: A pixel-topology-coupled guidewire tip segmentation framework for robot-assisted intervention,” International Journal of Computer Assisted Radiology and Surgery, vol. 17, no. 2, pp. 329–341, 2022. [Paper]

    Graphical abstract of PixelTopoIS iterative guidewire segmentation with coupled pixel and topology loss, endpoint localization, and loop detection

  • J. Zhu, H. Li, D. Ai*, Q. Yang, J. Fan, Y. Huang, H. Song, Y. Han, and J. Yang*, “Iterative closest graph matching for non-rigid 3d/2d coronary arteries registration,” Computer Methods and Programs in Biomedicine, vol. 199, p. 105 901, 2021. [Paper]

  • J. Yang, J. Zhu, D. Y. Sze, L. Cui, X. Li, Y. Bai, D. Ai, J. Fan, H. Song, and F. Duan*, “Feasibility of augmented reality–guided transjugular intrahepatic portosystemic shunt,” Journal of Vascular and Interventional Radiology, vol. 31, no. 12, pp. 2098–2103, 2020. [Paper]

    Graphical abstract of CT-based vessel modeling, electromagnetic tracking, and augmented-reality guidance for TIPS puncture

  • J. Zhu, J. Fan, S. Guo, D. Ai, H. Song, C. Wang, S. Zhou, and J. Yang*, “Heuristic tree searching for pose-independent 3d/2d rigid registration of vessel structures,” Physics in Medicine & Biology, vol. 65, no. 5, p. 055 010, 2020. [Paper]

    Graphical abstract of heuristic tree search for pose-independent rigid registration between 3D CTA and 2D X-ray vessel structures

  • H. Fang, D. Ai*, W. Cong, S. Yang, J. Zhu, Y. Huang, H. Song, Y. Wang, and J. Yang*, “Topology optimization using multiple-possibility fusion for vasculature extraction,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 2, pp. 442–456, 2019. [Paper]

    Graphical abstract of vasculature extraction through initial centerlines, connection-probability estimation, and shortest-path gap filling

  • H. Fang, J. Zhu, D. Ai, Y. Huang, Y. Jiang, H. Song, Y. Wang, and J. Yang*, “Greedy soft matching for vascular tracking of coronary angiographic image sequences,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 5, pp. 1466–1480, 2019. [Paper]

    Graphical abstract of greedy graph search and soft batch matching for deformable vascular tracking across coronary angiograms

  • J. Zhu, X. Wang, S. Ma, J. Fan, S. Song, X. Ma, D. Ai, H. Song, Y. Jiang, Y. Wang, and J. Yang*, “Unbiased groupwise registration for shape prediction of foot scans,” Medical & Biological Engineering & Computing, vol. 57, pp. 1985–1998, 2019. [Paper]

  • T. Fu, Q. Li, J. Zhu, D. Ai, Y. Huang, H. Song, Y. Jiang, Y. Wang, and J. Yang*, “Sparse Deformation Prediction using Markove Decision Processes (MDP) for Non-rigid Registration of MR Image,” Computer Methods and Programs in Biomedicine, vol. 162, pp. 47–59, 2018. [Paper]

  • C. Wu, J. Yang*, J. Zhu, W. Cong, D. Ai, H. Song, X. Liang, and Y. Wang, “Hybrid constraint optimization for 3d subcutaneous vein reconstruction by near-infrared images,” Computer Methods and Programs in Biomedicine, vol. 163, pp. 123–133, 2018. [Paper]

  • J. Zhu, J. Yang*, J. Fan*, D. Ai, Y. Jiang, H. Song, and Y. Wang, “Accurate measurement of granary stockpile volume based on fast registration of multi-station scans,” Remote Sensing Letters, vol. 9, no. 6, pp. 569–577, 2018. [Paper]

  • J. Yang, J. Fan, T. Fu, D. Ai, J. Zhu, Q. Li*, and Y. Wang, “Convex hull matching and hierarchical decomposition for multimodality medical image registration,” Journal of X-Ray Science and Technology, vol. 23, no. 2, pp. 253–265, 2015. [Paper]

Conference Proceedings

  • P. Lyu, J. Zhang, L. Zhang, W. Liu*, C. Wang, and J. Zhu*, “Metaunetr: Rethinking token mixer encoding for efficient multi-organ segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2024, pp. 446–455.[Paper][Code]

  • J. Xiong, P. Lyu, T. Lin, K. Song, C. Wang, and J. Zhu*, “A highly efficient segmentation method for abdominal multi-organs on laptop,” in MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation, Springer, 2024, pp. 116–131. [Paper] [Code]

  • R. Yin, Y. Zhang, Z. Pan, J. Zhu, C. Wang, and B. Jia*, “SRPose: Two-view relative pose estimation with sparse keypoints,” in Computer Vision – ECCV 2024, Cham: Springer Nature Switzerland, 2025, pp. 88–107. [Paper] [Code]

  • P. Lyu, J. Xiong, W. Fang, W. Zhang, C. Wang, and J. Zhu*, “Advancing multi-organ and pan-cancer segmentation in abdominal ct scans through scale-aware and self-attentive modulation,” in MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation, Springer, 2023, pp. 84–101. [Paper][Code]

    Graphical abstract of the two-phase scale-aware and self-attentive abdominal organ and pan-cancer segmentation pipeline

  • C. Wang, J. Zhu, X. Li, S. Chen, Z. Chen, Y. Huang, Y. Wu, M. Zhan, and L. Lu*, “Vision servoing endovascular interventional robot system: Design and verification,” in 2024 IEEE International Conference on Cyborg and Bionic Systems (CBS), IEEE, 2024, pp. 126–132. [Paper]

    Graphical abstract of vision-servoed microwire segmentation, bending-energy estimation, safety constraints, and closed-loop endovascular robot control

  • P. Lyu, C. Wang, W. Fang, J. Xiong, X. Li, H. Luo, W. Liu*, and J. Zhu*, “Registration-propagated liver tumor segmentation for non-enhanced ct-based interventions,” in International Conference on Image and Graphics, Springer, 2023, pp. 124–136. [Paper]

    Graphical abstract of liver tumor segmentation in enhanced CT followed by nonrigid registration and mask propagation to intraoperative non-enhanced CT

  • J. Xiong, P. Lyu, W. Fang, X. Li, C. Wang, and J. Zhu*, “CAS_Next: Towards accurate and topology-preserving coronary artery segmentation in digital substraction angiography,” in Proceedings of the 2023 4th International Conference on Control, Robotics and Intelligent System, 2023, pp. 95–99. [Paper]

    Graphical abstract of CAS_Next using dynamic deformable convolution, global frequency filtering, and connectivity loss for topology-preserving coronary segmentation

  • G. Zhang, H.-C. Wong, C. Wang, J. Zhu*, L. Lu, and G.-J. Teng, “A Temporary Transformer Network for Guide-wire Segmentation,” in 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), IEEE, 2021, pp. 1–5. [Paper]

    Graphical abstract of a temporal transformer combining previous and current fluoroscopy frames for guidewire segmentation

  • J. Zhu, H. Luo, C. Wang, J. Lu, and G.-J. Teng*, “Intelligent and automatic treatment planning system for low-dose-rate brachytherapy of malignant hepatic tumors,” in Proceedings of the 2021 International Conference on Intelligent Medicine and Health, 2021, pp. 47–52. [Paper]

    Graphical abstract of automatic hepatic brachytherapy planning with organ segmentation, parameterized seeds, inverse dose optimization, and safe puncture paths

  • J. Zhu, C. Wang, G. Zhang, S. A. Teng, L. Lu, and G.-J. Teng*, “A multi-stage guidewire tip tracking framework for cardiovascular robotic interventions,” in 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), IEEE, 2021, pp. 1–6. [Paper]

    Graphical abstract of prediction, locking, tracking, and fold-back safety monitoring for a guidewire tip in robotic cardiovascular intervention

  • S. Guo, S. Tang, J. Zhu, J. Fan, D. Ai, H. Song, P. Liang, and J. Yang*, “Improved U-net for Guidewire Tip Segmentation in X-ray Fluoroscopy Images,” in Proceedings of the 2019 3rd International Conference on Advances in Image Processing, 2019, pp. 55–59. [Paper]

    Graphical abstract of guidewire-tip segmentation using an improved U-Net with dense blocks and connectivity supervision

  • J. Zhu, S. Song, S. Guo, D. Ai, J. Fan, H. Song, P. Liang, and J. Yang*, “Monte Carlo Tree Search for 3D/2D Registration of Vessel Graphs,” in 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, 2019, pp. 787–791. [Paper]

    Graphical abstract of Monte Carlo tree search for pose-independent matching and rigid registration between 3D and 2D vessel graphs

  • G. Hu, J. Zhu, Y. Hu, J. Yu, P. Liang, and J. Yang*, “Multi-modal Image Fusion based Anatomical Shape Model for Low-contrast Anterior Visual Pathway and Medial Rectus Muscle Segmentation in CT Images,” in Proceedings of the 2019 8th International Conference on Bioinformatics and Biomedical Science, 2019, pp. 40–46. [Paper]

    Graphical abstract of CT-MR fusion, statistical shape transfer, and feature-constrained fitting for AVP and medial rectus muscle segmentation

  • W. Cong, J. Yang*, W. Deng, and J. Zhu, “Automatic Radial Distortion Correction for Endoscope Image,” in 2013 6th International Congress on Image and Signal Processing (CISP), IEEE, vol. 2, 2013, pp. 932–937. [Paper]