Fluorescence-based enhanced reality for colorectal endoscopic surgery

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8545 LNCS, Issue , 2014, Pages 114-123

  Selka, F ^a,b   Agnus, V ^a   Nicolau, S ^a   Bessaid, A ^b   Soler, L ^a,c   Marescaux, J ^a,c   Diana, M ^c  

^a IRCAD   (France)

^b UNIVERSITY OF TLEMCEN   (Algeria)

^c IHU   (France)

Author keywords

augmented reality; feature tracking; non rigid registration

Indexed keywords

ALGORITHMS; AUGMENTED REALITY; COMPUTER VISION; ENDOSCOPY; IMAGE REGISTRATION; SURGERY;

COMPUTER VISION ALGORITHMS; ENDOSCOPIC SURGERY; FEATURE-TRACKING; FLUORESCENCE SIGNALS; IMAGE DEFORMATION; IN-VIVO; MINIMALLY INVASIVE SURGERY; NONRIGID REGISTRATION;

FLUORESCENCE;

EID: 84903698673     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-08554-8_12     Document Type: Conference Paper

References (12)

1. Ferlay, J., et al.: GLOBOCAN 2012 v1.0 Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 (2013)
2. Choi, H.K., Law, W.L., Ho, J.W.C.: Leakage after resection and intraperitoneal anastomosis for colorectal malignancy: analysis of risk factors. Diseases of the Colon and Rectum 49, 1719-1725 (2006)
3. Urbanavičius, L., et al.: How to assess intestinal viability during surgery: A review of techniques. World Journal of Gastrointestinal Surgery 3, 59-69 (2011)
4. Kudszus, S., et al.: Intraoperative laser fluorescence angiography in colorectal surgery: a noninvasive analysis to reduce the rate of anastomotic leakage. Langenbeck's Archives of Surgery 395, 1025-1030 (2010)
5. Diana, M., et al.: Enhanced-reality video fluorescence: A real-time assessment of intestinal viability. Annals of Surgery 259(4), 700-707 (2014)
6. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404-417. Springer, Heidelberg (2006)
7. Selka, F., Nicolau, S.A., Agnus, V., Bessaid, A., Marescaux, J., Soler, L.: Evaluation of endoscopic image enhancement for feature tracking: A new validation framework. In: Liao, H., Linte, C.A., Masamune, K., Peters, T.M., Zheng, G. (eds.) MIAR/AECAI 2013. LNCS, vol. 8090, pp. 75-85. Springer, Heidelberg (2013)
8. Leutenegger, S., et al.: BRISK: Binary Robust invariant scalable keypoints. In: 2011 International Conference on Computer Vision, pp. 2548-2555 (November 2011)
9. Yip, M., et al.: Tissue Tracking and Registration for Image-Guided Surgery. IEEE Transactions on Medical Imaging 31, 2169-2182 (2012)
10. Puerto-Souza, G., Mariottini, G.: A Fast and Accurate Feature-Matching Algorithm for Minimally-Invasive Endoscopic Images. IEEE Transactions on Medical Imaging (January 2013)
11. Schaefer, S., McPhail, T., Warren, J.: Image deformation using moving least squares. ACM Transactions on Graphics, TOG (2006)
12. Diana, M., et al.: Probe-based confocal laser endomicroscopy (cellvizio) and enhanced-reality for real-time assessment of intestinal microcirculation at the future anastomotic site. Accepted to SAGES (2014)