Vision-based Navigation in Image-Guided Interventions

Mirota, Daniel J.; Ishii, Masaru; Hager, Gregory D.

doi:10.1146/annurev-bioeng-071910-124757

Cited by 137Medical Image Segmentation TechniquesSurgical Simulation and TrainingAugmented Reality Applications

Vision-based Navigation in Image-Guided Interventions

Mirota, Daniel J., Ishii, Masaru, Hager, Gregory D.·2011·Annual Review of Biomedical Engineering

DOI 10.1146/annurev-bioeng-071910-124757

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Abstract

The trend toward minimally invasive surgical interventions has created new challenges for visualization during surgical procedures. However, at the same time, the introduction of high-definition digital endoscopy offers the opportunity to apply methods from computer vision to provide visualization enhancements such as anatomic reconstruction, surface registration, motion tracking, and augmented reality. This review provides a perspective on this rapidly evolving field. It first introduces the clinical and technical background necessary for developing vision-based algorithms for interventional applications. It then discusses several examples of clinical interventions where computer vision can be applied, including bronchoscopy, rhinoscopy, transnasal skull-base neurosurgery, upper airway interventions, laparoscopy, robotic-assisted surgery, and Natural Orifice Transluminal Endoscopic Surgery (NOTES). It concludes that the currently reported work is only the beginning. As the demand for minimally invasive procedures rises, computer vision in surgery will continue to advance through close interdisciplinary work between interventionists and engineers.

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Image-based navigation techniques do not require specialized equipment but rely on traditional intra-operative imaging.
“Image-based navigation techniques do not require specialized equipment but rely on traditional intra-operative imaging that enabled the minimally invasive technique in the first place.”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Mirota et al., 2011

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Shi, C., Luó, X., Qi, P., Li, T., Song, S., Najdovski, Z., Fukuda, T., & Ren, H. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2622361 · Cited by 423 · see citing works
The status of augmented reality in laparoscopic surgery as of 2016
Bernhardt, S., Nicolau, S., Soler, L., & Doignon, C. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.01.007 · Cited by 315 · see citing works
Vision-based and marker-less surgical tool detection and tracking: a review of the literature
Bouget, D., Allan, M., Stoyanov, D. V., & Jannin, P. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.09.003 · Cited by 283 · see citing works
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
Maier‐Hein, L., Mountney, P., Bartoli, A., Elhawary, H., Elson, D. S., Groch, A., Kolb, A., Rodrigues, M., Sorger, J., Speidel, S., et al. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.04.003 · Cited by 280 · see citing works
Recent Developments and Future Challenges in Medical Mixed Reality
Chen, L., Day, T. W., Tang, W. J., & John, N. W. (2017) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar.2017.29 · Cited by 160 · see citing works
Surgical spectral imaging
Clancy, N. T., Jones, G., Maier‐Hein, L., Elson, D. S., & Stoyanov, D. V. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101699 · Cited by 158 · see citing works
Content-based processing and analysis of endoscopic images and videos: A survey
Münzer, B., Schoeffmann, K., & Böszörményi, L. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-016-4219-z · Cited by 140 · see citing works
Toward Detection and Localization of Instruments in Minimally Invasive Surgery
Allan, M., Sebastien Ourselin, A., Thompson, S., Hawkes, D. J., Kelly, J. D., & Stoyanov, D. V. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2229278 · Cited by 139 · see citing works
An endoscopic 3D scanner based on structured light
Schmalz, C., Forster, F., Schick, A., & Angelopoulou, E. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.04.001 · Cited by 112 · see citing works
Augmented Reality Guided Laparoscopic Surgery of the Uterus
Collins, T., Pizarro, D., Gasparini, S., Bourdel, N., Chauvet, P., Canis, M. J., Calvet, L., & Bartoli, A. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3027442 · Cited by 107 · see citing works
The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective
Unberath, M., Gao, C., Hu, Y., Judish, M., Taylor, R. H., Armand, M., & Grupp, R. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.716007 · Cited by 102 · see citing works
“…the challenges of conventional surgical navigation systems that introduce dedicated tracking hardware and instrumentation as well as workﬂow alterations, the computer-assisted interventions community has contributed purely image-based alternatives to surgical navigation, e. g., [10, 11, 12, 13].”
Background
Surgical Vision
Stoyanov, D. V. (2011) · Annals of Biomedical Engineering · doi:10.1007/s10439-011-0441-z · Cited by 99 · see citing works
Application of robotics in gastrointestinal endoscopy: A review
Yeung, B. P. M. (2016) · World Journal of Gastroenterology · doi:10.3748/wjg.v22.i5.1811 · Cited by 82 · see citing works
Navigated Bronchoscopy
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Context-aware Augmented Reality in laparoscopic surgery
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Recent Trends, Technical Concepts and Components of Computer-Assisted Orthopedic Surgery Systems: A Comprehensive Review
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Visually Navigated Bronchoscopy using three cycle-Consistent generative adversarial network for depth estimation
Banach, A., King, F., Masaki, F., Tsukada, H., & Hata, N. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102164 · Cited by 61 · see citing works
Toward Long-Term and Accurate Augmented-Reality for Monocular Endoscopic Videos
Puerto-Souza, G. A., Cadeddu, J., & Mariottini, G. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2323999 · Cited by 57 · see citing works
A Fast and Accurate Feature-Matching Algorithm for Minimally-Invasive Endoscopic Images
Puerto-Souza, G. A., & Mariottini, G.-L. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2239306 · Cited by 55 · see citing works
Stereoscopic Scene Flow for Robotic Assisted Minimally Invasive Surgery
Stoyanov, D. V. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33415-3_59 · Cited by 49 · see citing works
Real-Time Range Imaging in Health Care: A Survey
Bauer, S., Seitel, A., Hofmann, H., Blum, T., Wasza, J., Balda, M., Meinzer, H., Navab, N., Joachim Hornegger, A., & Maier‐Hein, L. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-44964-2_11 · Cited by 49 · see citing works
Simultaneous Multi-Structure Segmentation and 3D Nonrigid Pose Estimation in Image-Guided Robotic Surgery
Nosrati, M. S., Rafeef Abugharbieh, A., Peyrat, J., Abinahed, J. A., Al-Alao, O., Al-Ansari, A. A., & Hamarneh, G. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2452907 · Cited by 49 · see citing works
Real-Time Dense Stereo Reconstruction Using Convex Optimisation with a Cost-Volume for Image-Guided Robotic Surgery
Chang, P.-L., Stoyanov, D. V., Davison, A. J., & Edwards, P. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40811-3_6 · Cited by 47 · see citing works
The NifTK software platform for image-guided interventions: platform overview and NiftyLink messaging
Clarkson, M. J., Zombori, G., Thompson, S., Totz, J., Song, Y., Espak, M., Johnsen, S., Hawkes, D. J., & Sebastien Ourselin, A. (2014) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-014-1124-7 · Cited by 46 · see citing works
Computer-Assisted Laparoscopic myomectomy by augmenting the uterus with pre-operative MRI data
Collins, T., Pizarro, D., Bartoli, A., Canis, M. J., & Bourdel, N. (2014) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar.2014.6948434 · Cited by 45 · see citing works
3D Surface reconstruction of transparent objects using laser scanning with LTFtF method
He, K., Sui, C., Huang, T., Dai, R., Lyu, C., & Liu, Y. (2021) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2021.106774 · Cited by 45 · see citing works
A Discriminative Structural Similarity Measure and its Application to Video-Volume Registration for Endoscope Three-Dimensional Motion Tracking
Luó, X., & Mori, K. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2307052 · Cited by 43 · see citing works
Photometric stereo endoscopy
Parot, V., Lim, D., González, G., Traverso, G., Nishioka, N. S., Vakoc, B. J., & Durr, N. J. (2013) · Journal of Biomedical Optics · doi:10.1117/1.jbo.18.7.076017 · Cited by 41 · see citing works
Automatic Segmentation of Multiple Structures in Knee Arthroscopy Using Deep Learning
Jonmohamadi, Y., Takeda, Y., Liu, F., Sasazawa, F., Maicas, G., Crawford, R., Roberts, J. M., Pandey, A. K., & Carneiro, G. (2020) · IEEE Access · doi:10.1109/access.2020.2980025 · Cited by 40 · see citing works
Silica/gold nanoplatform combined with a thermosensitive gel for imaging-guided interventional therapy in PDX of pancreatic cancer
Xing, L., Li, X., Xing, Z., Li, F., Shen, M., Wang, H., Shi, X., & Du, L. (2019) · Chemical Engineering Journal · doi:10.1016/j.cej.2019.122949 · Cited by 37 · see citing works
Surgical Simulation Training Systems: Box Trainers, Virtual Reality and Augmented Reality Simulators
Loukas, C. (2016) · International Journal of Advanced Robotics and Automation · doi:10.15226/2473-3032/1/2/00109 · Cited by 37 · see citing works
Clinical Application of Image-Enhanced Minimally Invasive Robotic Surgery for Gastric Cancer: A Prospective Observational Study
Kim, Y. M., Baek, S., Lim, J. S., & Hyung, W. J. (2012) · Journal of Gastrointestinal Surgery · doi:10.1007/s11605-012-2094-0 · Cited by 36 · see citing works
2D-3D Pose Tracking of Rigid Instruments in Minimally Invasive Surgery
Allan, M., Thompson, S., Clarkson, M. J., Sebastien Ourselin, A., Hawkes, D. J., Kelly, J. D., & Stoyanov, D. V. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07521-1_1 · Cited by 34 · see citing works
Augmented reality visualization of deformable tubular structures for surgical simulation
Ferrari, V., Viglialoro, R. M., Nicoli, P., Cutolo, F., Condino, S., Carbone, M., Siesto, M., & Ferrari, M. (2015) · International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.1681 · Cited by 33 · see citing works
Vision-based endoscope tracking for 3D ultrasound image-guided surgical navigation
Yang, L., Wang, J., Ando, T., Kubota, A., Yamashita, H., Sakuma, I., Chiba, T., & Kobayashi, E. (2014) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2014.09.003 · Cited by 32 · see citing works
Patient Registration Using Intraoperative Stereovision in Image-guided Open Spinal Surgery
Ji, S., Fan, X., Paulsen, K. D., Roberts, D. W., Mirza, S. K., & Lollis, S. S. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2415731 · Cited by 30 · see citing works
Current and emerging robotic assisted intervention for Notes
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Image-guided robotic surgery
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CT-Guided Interventions Using a Free-Hand, Optical Tracking System: Initial Clinical Experience
Schubert, T. B., Jacob, A. L., Pansini, M., Liu, D. M., Gutzeit, A., & Kos, S. (2012) · CardioVascular and Interventional Radiology · doi:10.1007/s00270-012-0527-5 · Cited by 28 · see citing works
Review on vision‐based tracking in surgical navigation
Yang, L., & Kobayashi, E. (2020) · IET Cyber-Systems and Robotics · doi:10.1049/iet-csr.2020.0013 · Cited by 26 · see citing works
Threshold-Guided Design and Optimization for Harris Corner Detector Architecture
Jasani, B. A., Lam, S.-K., Meher, P. K., & Wu, M. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2757998 · Cited by 25 · see citing works
A Real-Time 3D Laparoscopic Imaging System: Design, Method, and Validation
Sui, C., Wu, J., Wang, Z., Ma, G., & Liu, Y. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.2968488 · Cited by 24 · see citing works
Dense Depth Estimation from Stereo Endoscopy Videos Using Unsupervised Optical Flow Methods
Yang, Z., Simon, R. A., Li, Y., & Linte, C. A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80432-9_26 · Cited by 24 · see citing works
ASSESSMENT OF SAFETY RISKS USING QUALIMETRIC METHODS
Trishch, R., Nechuiviter, O. P., Hrinchenko, H., Бубела, Т. З., Riabchykov, М., & Pandová, I. (2023) · MM Science Journal · doi:10.17973/mmsj.2023_10_2023021 · Cited by 24 · see citing works
The Use of Artificial Intelligence and Machine Learning in Surgery: A Comprehensive Literature Review
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3D surface reconstruction of transparent objects using laser scanning with a four-layers refinement process
He, K., Sui, C., Huang, T., Zhang, Y., Zhou, W., Chen, X., & Liu, Y.-H. (2022) · Optics Express · doi:10.1364/oe.449300 · Cited by 21 · see citing works
Endoscopic Vision Augmentation Using Multiscale Bilateral-Weighted Retinex for Robotic Surgery
Luo, X., Zeng, H., Wan, Y., Zhang, X., Du, Y., & Peters, T. M. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2916101 · Cited by 21 · see citing works
Force-Controlled Exploration for Updating Virtual Fixture Geometry in Model-Mediated Telemanipulation
Wang, L., Chen, Z., Chalasani, P., Yasin, R., Kazanzides, P., Taylor, R. H., & Simaan, N. (2017) · Journal of Mechanisms and Robotics · doi:10.1115/1.4035684 · Cited by 20 · see citing works
Endoscopic Navigation in the Absence of CT Imaging
Sinha, A., Liu, X., Reiter, A., Ishii, M., Hager, G. D., & Taylor, R. H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00937-3_8 · Cited by 18 · see citing works
Self-citation
Endoscopic image feature matching via motion consensus and global bilateral regression
Chu, Y., Li, H., Li, X., Ding, Y., Yang, X., Ai, D., Chen, X., Wang, Y., & Yang, J. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105370 · Cited by 17 · see citing works
Structured Light Techniques for 3D Surface Reconstruction in Robotic Tasks
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An intelligent real time 3D vision system for robotic welding tasks
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Enhanced low-complexity pruning for corner detection
Ramakrishnan, N., Wu, M., Lam, S.-K., & Srikanthan, T. (2014) · Journal of Real-Time Image Processing · doi:10.1007/s11554-014-0396-z · Cited by 16 · see citing works
Towards scene adaptive image correspondence for placental vasculature mosaic in computer assisted fetoscopic procedures
Yang, L., Wang, J., Ando, T., Kubota, A., Yamashita, H., Sakuma, I., Chiba, T., & Kobayashi, E. (2015) · International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.1700 · Cited by 15 · see citing works
Efficient Stereo Image Geometrical Reconstruction at Arbitrary Camera Settings from a Single Calibration
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Bayesian Estimation of Intrinsic Tissue Oxygenation and Perfusion From RGB Images
Jones, G., Clancy, N. T., Helo, Y., Arridge, S., Elson, D. S., & Stoyanov, D. V. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2665627 · Cited by 15 · see citing works
A lumen-adapted navigation scheme with spatial awareness from monocular vision for autonomous robotic endoscopy
Yang, T., Yang, Y., Wang, P., Cao, Y., Yang, Z. L., & Liu, H. (2023) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2023.104444 · Cited by 14 · see citing works
Automated thresholding for low-complexity corner detection
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Real-time structured light-based otoscopy for quantitative measurement of eardrum deformation
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A Monocular Variable Magnifications 3D Laparoscope System Using Double Liquid Lenses
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Monocular endoscope 6-DoF tracking with constrained evolutionary stochastic filtering
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How big is this neoplasia? Live colonoscopic size measurement using the Infocus-Breakpoint
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Externally Navigated Bronchoscopy Using 2-D Motion Sensors: Dynamic Phantom Validation
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Endoscopic Laser Surface Scanner for Minimally Invasive Abdominal Surgeries
Geurten, J., Xia, W., Jayarathne, U. L., Peters, T., & Chen, E. C. S. (2018) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-030-00937-3_17 · Cited by 11 · see citing works
Adversarial Diffusion Model for Domain-Adaptive Depth Estimation in Bronchoscopic Navigation
Yang, Y., Ning, G., Zhong, C., & Liao, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72089-5_5 · Cited by 9 · see citing works
AUTONOMOUS MOBILE ROBOT PATH PLANNING BASED ON ENHANCED A* ALGORITHM INTEGRATING WITH TIME ELASTIC BAND
Dang, T.-V. (2023) · MM Science Journal · doi:10.17973/mmsj.2023_10_2023052 · Cited by 8 · see citing works
Navigation Path Retrieval from Videobronchoscopy Using Bronchial Branches
Sánchez, C., Díez-Ferrer, M., Bernal, J., Sánchez, F. J., Rosell, A., & Gil, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-31808-0_8 · Cited by 8 · see citing works
Computer-assisted 3D bowel length measurement for quantitative laparoscopy
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DEVELOPMENT OF SMART DISINFECTION MACHINE USING IOT TECHNOLOGY
Phuc, T. D. H., THANH, T. V., CUONG, L. T., Vũ, N., & Bach, D. T. (2023) · MM Science Journal · doi:10.17973/mmsj.2023_10_2023057 · Cited by 7 · see citing works
Using the Infocus-Breakpoint to estimate the scale of neoplasia in colonoscopy
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Enhanced Vision to Improve Safety in Robotic Surgery
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Investigating keypoint descriptors for camera relocalization in endoscopy surgery
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Self-citation
Deep learning-based automatic pipeline for 3D needle localization on intra-procedural 3D MRI
Zhou, W., Li, X., Zabihollahy, F., Lu, D., & Wu, H. H. (2024) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-024-03077-3 · Cited by 6 · see citing works
Self-adaptive corner detection on MPSoC through resource-aware programming
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Robotic Urology
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A 3D Laparoscopic Imaging System Based on Stereo-Photogrammetry with Random Patterns
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Navigated non-contact fluorescence tomography
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A comparative study on 3-D stereo reconstruction from endoscopic images
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OPTIMIZATION OF PROCESS PARAMETERS FOR WOOD LASER ENGRAVING BASED ON TAGUCHI METHOD APPROACH
Ngoc, H. N. T., & Dang, T.-V. (2023) · MM Science Journal · doi:10.17973/mmsj.2023_10_2023053 · Cited by 5 · see citing works
Modular multiple sensors information management for computer‐integrated surgery
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Development of a Visual Guidance System for Laparoscopic Surgical Palpation using Computer Vision
Caballas, K. G., Bolingot, H. J. M., Libatique, N. J. C., & Tangonan, G. L. (2021) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes48179.2021.9398796 · Cited by 5 · see citing works
Image Guidance in Robotic-Assisted Renal Surgery
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Toward real-time remote processing of laparoscopic video
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Underwater 3D Imaging Using a Fiber-Based Endoscopic System for Arthroscopic Surgery
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Resonantly loaded apertures for sub‐diffraction near‐field imaging and surface probing
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3D TECHNOLOGIES FOR INTANGIBLE CULTURAL HERITAGE PRESERVATION: CREATING 3D MODELS OF LACQUER ART THROUGH PHOTOGRAMMETRY
Nguyen, H.-S., Hoang, K.-D. H., Huynh, C.-D., Ma, Q.-P., & Měsíček, J. (2023) · MM Science Journal · doi:10.17973/mmsj.2023_10_2023033 · Cited by 4 · see citing works
Augmenting interventional ultrasound using statistical shape model for guiding percutaneous nephrolithotomy: Initial evaluation in pigs
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Non Rigid Registration of 3D Images to Laparoscopic Video for Image Guided Surgery
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Error Analysis and Calibration Method of a Multiple Field-of-View Navigation System
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Robotic Automation for Surgery
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POSITIONING PERFORMANCE OF LOW-COST U-BLOX NEO-M8U MODULE IN URBAN ENVIRONMENT
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Authors: Mirota, D. J., Ishii, M., & Hager, G. D.
Affiliations: Johns Hopkins University (US)
ORCID: Ishii, Masaru
Hager, Gregory D.
Venue: Annual Review of Biomedical Engineering
Publisher: Annual Reviews
Published: 2011-08-15
Volume: 13
Issue: 1
Pages: 297-319
ISSN: 1523-9829 (ISSN-L)
1545-4274
DOI: 10.1146/annurev-bioeng-071910-124757
OpenAlex: W2146082292
Cited by: 137
References: 98
Citations by year: 2026: 2 · 2025: 2 · 2024: 2 · 2023: 35 · 2022: 3 · 2021: 7 · 2020: 8 · 2019: 5 · 2018: 4 · 2017: 11 · 2016: 10 · 2015: 8 · 2014: 18 · 2013: 13 · 2012: 7
FWCI: 2.72
Type: journal article
Language: en
Open access: closed
Locations: Annual Review of Biomedical Engineering (publishedVersion)
PubMed (publishedVersion)
Funders: National Science Foundation
National Institutes of Health
National Cancer Institute (R01-CA127144)
National Institute of Biomedical Imaging and Bioengineering (R21-EB005201-01A, R21EB008490, R21EB005201)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Health Sciences › Medicine › Surgery › Surgical Simulation and Training
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
Keywords: visual navigation, image-guided surgery, minimally invasive surgery, augmented reality, surface registration, motion tracking, anatomical reconstruction, bronchoscopy, rhinoscopy, laparoscopy, robotic surgery, natural orifice transluminal endoscopic surgery, endoscopy
MeSH terms: Algorithms, Bronchoscopy, Diagnostic Imaging, Humans, Laparoscopy, Robotics, Neurosurgical Procedures, Video-Assisted Surgery, Surgery, Computer-Assisted, Natural Orifice Endoscopic Surgery

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