The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
Abstract
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon's navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D optical imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions.
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Cites
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
Parallel Tracking and Mapping for Small AR Workspaces
Klein, G., & Murray, D. (2007) · 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2007.4538852 · Cited by 4,346
Simultaneous localization and mapping: part I
Durrant-Whyte, H., & Bailey, T. (2006) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2006.1638022 · Cited by 4,199
DTAM: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126513 · Cited by 2,078 · cited 3×
“Dense Tracking And Matching (DTAM) (Newcombe et al., 2011) builds on this approach to produce dense maps and robust camera localization.”
BackgroundMöbius voting for surface correspondence
Lipman, Y., & Funkhouser, T. (2009) · ACM Transactions on Graphics · doi:10.1145/1531326.1531378 · Cited by 332
Validation of medical image processing in image-guided therapy
Jannin, P., Fitzpatrick, J. M., Hawkes, D. J., Pennec, X., Shahidl, R., & Vannier, M. W. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.806568 · Cited by 177
Vision-based Navigation in Image-Guided Interventions
Mirota, D. J., Ishii, M., & Hager, G. D. (2011) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071910-124757 · Cited by 137
Fusion of range and color images for denoising and resolution enhancement with a non-local filter
Huhle, B., Schairer, T., Jenke, P., & Straßer, W. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2009.11.004 · Cited by 116 · cited 1×
“In the nonendoscopic context, the approaches proposed so far have focussed on combining ToF sensors with one or more RGB cameras with the purpose of increasing depth accuracy (Zhu et al., 2011; Fischer et al., 2011; Huhle et al., 2010; Gudmundsson et al., 2008) or resolution (Henry et al.”
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Cited by
Shape Sensing Techniques for Continuum Robots in Minimally Invasive Surgery: A Survey
Shi, C., Luo, X., Qi, P., Li, T., Song, S., Najdovski, Z., Fukuda, T., & Ren, H. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2622361 · Cited by 420
Visual SLAM for Handheld Monocular Endoscope
Grasa, O. G., Bernal, E., Casado, S., Gil, I., & Montiel, J. M. M. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2282997 · Cited by 155
Metadata
- Authors
- Maier-Hein, L., Mountney, P., Bartoli, A., Elhawary, H., Elson, D., Groch, A., Kolb, A., Rodrigues, M., Sorger, J., Speidel, S., & Stoyanov, D.
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2013-12
- Volume
- 17
- Issue
- 8
- Pages
- 974-996
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2148687996
- Cited by
- 280
- References
- 9
- FWCI
- 28.25
- Type
- journal article
- Language
- en
- Open access
- closed
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- Maier-Hein, L., Mountney, P., Bartoli, A., Elhawary, H., Elson, D., Groch, A., Kolb, A., Rodrigues, M., Sorger, J., Speidel, S., & Stoyanov, D. (2013). Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery. Medical Image Analysis. https://doi.org/10.1016/j.media.2013.04.003
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