The DOI matches its Crossref record; 98 of the 98 references it cites were found in a registry or an index.
Vision-Based Navigation in Image-Guided Interventions
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 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
Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
A Method for Registration of 3-D Shapes
Besl, P., & Mckay, N. D. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121791 · Cited by 22,163
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
A flexible new technique for camera calibration
Zhang, Z. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.888718 · Cited by 15,004
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
(1981) · Cited by 11,963
Shi, J., & Tomasi (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323794 · Cited by 7,724
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Scharstein, D., & Szeliski, R. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014573219977 · Cited by 6,740
Tsai, R. (1987) · IEEE Journal on Robotics and Automation · doi:10.1109/jra.1987.1087109 · Cited by 5,890
Bundle Adjustment — A Modern Synthesis
Triggs, B., McLauchlan, P. F., Hartley, R. I., & Fitzgibbon, A. W. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-44480-7_21 · Cited by 3,988
Research works printed but not linked: 14
- Désormeaux AJ., 1855, C.R. Hébd. Séances Acad. Sci., 40, 692
- Shi J, 1994, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn., June 21–23, Seattle, 593
- Takeshita T, 2008, Proc. ASIAGRAPH 2008, Shanghai, 190
- Ng R, 2005, Comput. Sci. Tech. Rep. CTSR 2005-02
- Kolar A, 2010, EURASIP J. Embed. Syst., 2009, 716317
- Höller K, 2008, Biosignal-verarbeitung, 180
- Nolte LP, 1994, Proc. First Int. Symp. Med. Robot. Comput. Assist. Surg., Sept. 22–24, Pittsburgh, Pa., 323
- 59. Bucholz RD, Smith KR. 1993. A comparison of sonic digitizers versus light emitting diode-based localization. See Reference 57, pp. 179–200
- Taylor RH, 1992, Innov. Technol. Biol. Med., 13, 450
- Hofstetter R, 1999, Proc. Comput. Assist. Orthop. Surg. Symp., 4th, Davos, Switz., 28
- Poulin F, 2001, Proc. Comput. Assist. Orthop. Surg. Symp. USA, July 6–8, Pittsburgh, Pa., 233
- Reinhardt HF., 1996, Computer-Integrated Surgery, 329
- Lucas B, 1981, Proc. Int. Jt. Conf. Artif. Intell., Aug. 24–28, Vancouver, Can., 674
- 106. Höller K, Hempel S, Engelbrecht R, Hornegger J, Feussner H. 2010. Distance calibration with a time-of-flight 3-D endoscope. Presented at Russ. Bavar. Conf. Biomed. Eng., 6th, Moscow
Cited by
Sorted by citations
Emerging Robotic Platforms for Minimally Invasive Surgery
Vitiello, V., Su-Lin, L., Cundy, T. P., & Yang, G. (2012) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2012.2236311 · Cited by 455
Shape Sensing Techniques for Continuum Robots in Minimally Invasive Surgery: A Survey
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
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
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 292
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
Metadata
- Authors
- Mirota, D. J., Ishii, M., & Hager, G. D.
- Affiliations
- Johns Hopkins University (US)
- 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
- 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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