Abstract
Robotizing flexible endoscopy enables image-based control of endoscopes. Especially during high-throughput procedures, such as a colonoscopy, navigation support algorithms could improve procedure turnaround and ergonomics for the endoscopist.
In this study, we have developed and implemented a navigation algorithm that is based on image classification followed by dark region segmentation. Robustness and accuracy were evaluated on real images obtained from human colonoscopy exams. Comparison was done using manual annotation as a reference. Intraclass correlation (ICC) was employed as a measure for similarity between automated and manual results.
The discrimination of the developed classifier was 6.8, making it a reliable classifier. In the experiments, the developed algorithm gave an ICC of 93 % (range 84.7–98.8 %) over the test image sequences on average. If images were classified as ‘uninformative’, which led to re-initialization of the algorithm, this was predictive for the result of dark region segmentation accuracy.
In conclusion, the developed target detection algorithm provided accurate results and is thought to provide reliable assistance in the clinic. The clinical relevance of this kind of navigation and control is currently being investigated.
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References
Taylor, R.H.: A perspective on medical robotics. Proc. IEEE 94(9), 1652–1664 (2006)
Pedrosa, M.C., Farraye, F.A., et al.: Minimizing occupational hazards in endoscopy: personal protective equipment, radiation safety, and ergonomics. Gastrointest. Endosc. 72(2), 227–235 (2010)
Ruiter, J., Rozeboom, E., et al.: Design and evaluation of robotic steering of a flexible endoscope. In: IEEE BioRob, pp. 761–767 (2012)
Van Der Stap, N., Van Der Heijden, F., Broeders, I.A.M.J.: Towards automated visual flexible endoscope navigation. Surg. Endosc. 27(10), 1–13 (2013)
Waye, J.D., Rex, D.K., Williams, C.B.: Colonoscopy: Principles and Practice, 2nd edn, pp. 267–345. Blackwell Publishing Ltd, Chichester (2009)
Lee, S.-H., Chung, I.-K., et al.: An adequate level of training for technical competence in screening and diagnostic colonoscopy: a prospective multicenter evaluation of the learning curve. Gastrointest. Endosc. 67(4), 683–689 (2008)
Gillies, D., Khan, G.: Vision based navigation system for an endoscope. Image Vis. Comput. 14, 763–772 (1996)
Reilink, R., Stramigioli, S., Misra, S.: Image-based flexible endoscope steering. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. i, p. 6 (2010)
Van Der Heijden, F., Duin, R.P.W., et al.: Classification, Parameter Estimation and State Estimation, pp. 45–79. Wiley, Chichester (2004)
Duin, R.P.W., Tax, D.M.J.: PRTools – a matlab toolbox for pattern recognition, 14 July, 2013. http://prtools.org. Accessed 15 May 2014
van der Stap, N., Reilink, R., et al.: The use of the focus of expansion for automated steering of flexible endoscopes. In: IEEE BioRob, pp. 13–18 (2012)
Jellinek, E.M.: On the use of the intra-class correlation coefficient in the testing of the difference of certain variance ratios. J. Educ. Psychol. 31(1), 60–63 (1940)
Chettaoui, H., Thomann, G., et al.: Extracting and tracking Colon’s ‘Pattern’ from Colonoscopic Images. In: IEEE Canadian Conference on Computer Robot Vision, pp. 65–71 (2006)
Zhiyun, X.: Computerized detection of abnormalities in endoscopic oesophageal images. Ph.D. thesis, Nanyang Technological University (2000)
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van der Stap, N., Slump, C.H., Broeders, I.A.M.J., van der Heijden, F. (2014). Image-Based Navigation for a Robotized Flexible Endoscope. In: Luo, X., Reichl, T., Mirota, D., Soper, T. (eds) Computer-Assisted and Robotic Endoscopy. CARE 2014. Lecture Notes in Computer Science(), vol 8899. Springer, Cham. https://doi.org/10.1007/978-3-319-13410-9_8
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DOI: https://doi.org/10.1007/978-3-319-13410-9_8
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