Abstract
Robust visual tracking of instruments is an important task in retinal microsurgery. In this context, the instruments are subject to a large variety of appearance changes due to illumination and other changes during a procedure, which makes the task very challenging. Most existing methods require collecting a sufficient amount of labelled data and yet perform poorly in handling appearance changes that are unseen in training data. To address these problems, we propose a new approach for robust instrument tracking. Specifically, we adopt an online learning technique that collects appearance samples of instruments on the fly and gradually learns a target-specific detector. Online learning enables the detector to reinforce its model and become more robust over time. The performance of the proposed method has been evaluated on a fully annotated dataset of retinal instruments in in-vivo retinal microsurgery and on a laparoscopy image sequence. In all experimental results, our proposed tracking approach shows superior performance compared to several other state-of-the-art approaches.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Pezzementi, Z., Voros, S., Hager, G.D.: Articulated object tracking by rendering consistent appearance parts. In: IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 3940–3947 (2009)
Sznitman, R., Basu, A., Richa, R., Handa, J., Gehlbach, P., Taylor, R.H., Jedynak, B., Hager, G.D.: Unified detection and tracking in retinal microsurgery. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 1–8. Springer, Heidelberg (2011)
Burschka, D., Corso, J.J., Dewan, M., Lau, W., Li, M., Lin, H., Marayong, P., Ramey, N., Hager, G.D., Hoffman, B., et al.: Navigating inner space: 3-D assistance for minimally invasive surgery. Robotics and Autonomous Systems 52(1), 5–26 (2005)
Richa, R., Balicki, M., Meisner, E., Sznitman, R., Taylor, R., Hager, G.: Visual tracking of surgical tools for proximity detection in retinal surgery. In: Taylor, R.H., Yang, G.-Z. (eds.) IPCAI 2011. LNCS, vol. 6689, pp. 55–66. Springer, Heidelberg (2011)
Sznitman, R., Ali, K., Richa, R., Taylor, R.H., Hager, G.D., Fua, P.: Data-driven visual tracking in retinal microsurgery. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 568–575. Springer, Heidelberg (2012)
Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: BMVC, vol. 1, p. 6 (2006)
Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: IEEE Computer Vision and Pattern Recognition (CVPR), pp. 983–990 (2009)
Liu, B., Huang, J., Yang, L., Kulikowsk, C.: Robust tracking using local sparse appearance model and k-selection. In: IEEE Computer Vision and Pattern Recognition (CVPR), pp. 1313–1320 (2011)
Liu, B., Yang, L., Huang, J., Meer, P., Gong, L., Kulikowski, C.: Robust and fast collaborative tracking with two stage sparse optimization. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 624–637. Springer, Heidelberg (2010)
Sznitman, R., Richa, R., Taylor, R.H., Jedynak, B., Hager, G.D.: Unified detection and tracking of instruments during retinal microsurgery. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(5), 1263–1273 (2013)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(7), 1409–1422 (2012)
Kalal, Z., Mikolajczyk, K., Matas, J.: Forward-backward error: Automatic detection of tracking failures. In: 20th International Conference on Pattern Recognition (ICPR), pp. 2756–2759 (2010)
Baker, S., Matthews, I.: Lucas-kanade 20 years on: A unifying framework. International Journal of Computer Vision 56(3), 221–255 (2004)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. I-511–I-518 (2001)
Ozuysal, M., Fua, P., Lepetit, V.: Fast keypoint recognition in ten lines of code. In: IEEE Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2007)
Pickering, M.R., Muhit, A.A., Scarvell, J.M., Smith, P.N.: A new multi-modal similarity measure for fast gradient-based 2d-3d image registration. In: IEEE Engineering in Medicine and Biology Society, EMBC 2009, pp. 5821–5824 (2009)
Benhimane, S., Malis, E.: Homography-based 2d visual tracking and servoing. The International Journal of Robotics Research 26(7), 661–676 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Li, Y., Chen, C., Huang, X., Huang, J. (2014). Instrument Tracking via Online Learning in Retinal Microsurgery. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8673. Springer, Cham. https://doi.org/10.1007/978-3-319-10404-1_58
Download citation
DOI: https://doi.org/10.1007/978-3-319-10404-1_58
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10403-4
Online ISBN: 978-3-319-10404-1
eBook Packages: Computer ScienceComputer Science (R0)