Abstract
Optical colonoscopy is performed by insertion of a long flexible colonoscope into the colon. Estimating the position of the colonoscope tip with respect to the colon surface is important as it would help localization of cancerous polyps for subsequent surgery and facilitate navigation. Knowing camera pose is also essential for 3D automatic scene reconstruction, which could support clinicians inspecting the whole colon surface thereby reducing missed polyps. This paper presents a method to estimate the pose of the colonoscope camera with six degrees of freedom (DoF) using deep convolutional neural network (CNN). Because obtaining a ground truth to train the CNN for camera pose from actual colonoscopy videos is extremely challenging, we trained the CNN using realistic synthetic videos generated with a colonoscopy simulator, which could generate the exact camera pose parameters. We validated the trained CNN on unseen simulated video datasets and on actual colonoscopy videos from 10 patients. Our results showed that the colonoscopy camera pose could be estimated with higher accuracy and speed than feature based computer vision methods such as the classical structure from motion (SfM) pipeline. This paper demonstrates that transfer learning from surgical simulation to actual endoscopic based surgery is a possible approach for deep learning technologies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Australian Institute of Health and Welfare. http://www.aihw.gov.au/
World Health Organization (WHO). Fact sheet # 297: Cancer. http://www.who.int/mediacentre/factsheets/fs297/en/
Hewett, D.G., Kahi, C.J., Rex, D.K.: Does colonoscopy work? J. Natl. Compr. Cancer Netw. JNCCN 8, 67–76 (2010). quiz 77
Cotton, P.B., Williams, C.B.: Practical Gastrointestinal Endoscopy. Wiley-Blackwell, Oxford (2008)
Puerto-Souza, G.A., Staranowicz, A.N., Bell, C.S., Valdastri, P., Mariottini, G.-L.: A comparative study of ego-motion estimation algorithms for teleoperated robotic endoscopes. In: Luo, X., Reichl, T., Mirota, D., Soper, T. (eds.) CARE 2014. LNCS, vol. 8899, pp. 64–76. Springer, Cham (2014). doi:10.1007/978-3-319-13410-9_7
Liu, J., Subramanian, K.R., Yoo, T.S.: A robust method to track colonoscopy videos with non-informative images. Int. J. Comput. Assist. Radiol. Surg. 8, 575–592 (2013)
Armin, M.A., Chetty, G., De Visser, H., Dumas, C., Grimpen, F., Salvado, O.: Automated visibility map of the internal colon surface from colonoscopy video. Int. J. Comput. Assist. Radiol. Surg. 11, 1599–1610 (2016)
Rai, L., Helferty, J.P., Higgins, W.E.: Combined video tracking and image-video registration for continuous bronchoscopic guidance. Int. J. Comput. Assist. Radiol. Surg. 3, 315–329 (2008)
Bao, G., Pahlavan, K., Mi, L.: Hybrid localization of microrobotic endoscopic capsule inside small intestine by data fusion of vision and RF sensors. IEEE Sens. J. 15, 2669–2678 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Aubry, M., Maturana, D., Efros, A.A., Russell, B.C., Sivic, J.: Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD Models, June 2014
Dosovitskiy, A., Fischery, P., Ilg, E., Hazirbas, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T.: Flownet: learning optical flow with convolutional networks. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 2758–2766. IEEE (2015)
Zhou, T., Krähenbühl, P., Aubry, M., Huang, Q., Efros, A.A.: Learning Dense Correspondence via 3D-guided Cycle Consistency. ArXiv Prepr. arXiv:1604.05383 (2016)
Bell, C.S., Obstein, K.L., Valdastri, P.: Image partitioning and illumination in image-based pose detection for teleoperated flexible endoscopes. Artif. Intell. Med. 59, 185–196 (2013)
Kendall, A., Grimes, M., Cipolla, R.: Convolutional networks for real-time 6-DOF camera relocalization. Proceedings of the International Conference on Computer Vision (ICCV) (2015)
Su, H., Qi, C.R., Li, Y., Guibas, L.J.: Render for CNN: viewpoint estimation in images using CNNs trained with rendered 3D model views. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2686–2694 (2015)
Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., Brox, T.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Armin, M.A., De Visser, H., Chetty, G., Dumas, C., Conlan, D., Grimpen, F., Salvado, O.: Visibility map: a new method in evaluation quality of optical colonoscopy. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 396–404. Springer, Cham (2015). doi:10.1007/978-3-319-24553-9_49
Liu, C., Yuen, J., Torralba, A., Sivic, J., Freeman, W.T.: SIFT flow: dense correspondence across different scenes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 28–42. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88690-7_3
Armin, M.A., Chetty, G., Jurgen, F., De Visser, H., Dumas, C., Fazlollahi, A., Grimpen, F., Salvado, O.: Uninformative frame detection in colonoscopy through motion, edge and color features. In: Luo, X., Reichl, T., Reiter, A., Mariottini, G.-L. (eds.) CARE 2015. LNCS, vol. 9515, pp. 153–162. Springer, Cham (2016). doi:10.1007/978-3-319-29965-5_15
Huynh, D.Q.: Metrics for 3D rotations: comparison and analysis. J. Math. Imaging Vis. 35, 155–164 (2009)
Vedaldi, A., Lenc, K.: MatConvNet: Convolutional Neural Networks for MATLAB (2015)
De Visser, H., Passenger, J., Conlan, D., Russ, C., Hellier, D., Cheng, M., Acosta, O., Ourselin, S., Salvado, O.: Developing a next generation colonoscopy simulator. Int. J. Image Graph. 10, 203–217 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Armin, M.A., Barnes, N., Alvarez, J., Li, H., Grimpen, F., Salvado, O. (2017). Learning Camera Pose from Optical Colonoscopy Frames Through Deep Convolutional Neural Network (CNN). In: Cardoso, M., et al. Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures. CARE CLIP 2017 2017. Lecture Notes in Computer Science(), vol 10550. Springer, Cham. https://doi.org/10.1007/978-3-319-67543-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-67543-5_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67542-8
Online ISBN: 978-3-319-67543-5
eBook Packages: Computer ScienceComputer Science (R0)