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Targeted Optical Biopsies for Surveillance Endoscopies

  • Selen Atasoy
  • Diana Mateus
  • Alexander Meining
  • Guang-Zhong Yang
  • Nassir Navab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

Recent introduction of probe-based confocal laser endomicroscopy (pCLE) allowed for the acquisition of in-vivo optical biopsies during the endoscopic examination without removing any tissue sample. The non-invasive nature of the optical biopsies makes the re-targeting of previous biopsy sites in surveillance examinations difficult due to the absence of scars or surface landmarks. In this work, we introduce a new method for recognition of optical biopsy scenes of the diagnosis endoscopy during serial surveillance examinations. To this end, together with our clinical partners, we propose a new workflow involving two-run surveillance endoscopies to reduce the ill-posedness of the task. In the first run, the endoscope is guided from the mouth to the z-line (junction from the oesophagus to the stomach). Our method relies on clustering the frames of the diagnosis and the first run surveillance (\( \mathcal{S}1 \)) endoscopy into several scenes and establishing cluster correspondences accross these videos. During the second run surveillance (\( \mathcal{S}2 \)), the scene recognition is performed in real-time and in-vivo based on the cluster correspondences. Detailed experimental results demonstrate the feasibility of the proposed approach with 89.75% recall and 80.91% precision on 3 patient datasets.

Keywords

Visual Similarity Surveillance Endoscopy Diagnosis Endoscopy Manifold Representation Scene Recognition 
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

  1. 1.
    Allain, B., Hu, M., Lovat, L., Cook, R., Ourselin, S., Hawkes, D.: Biopsy Site Re-localisation Based on the Computation of Epipolar Lines from Two Previous Endoscopic Images. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 491–498. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Allain, B., Hu, M., Lovat, L., Cook, R., Vercauteren, T., Ourselin, S., Hawkes, D.: A System for Biopsy Site Re-targeting with Uncertainty in Gastroenterology and Oropharyngeal Examinations. In: Jiang, T., Navab, N., Pluim, J., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 514–521. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Atasoy, S., Glocker, B., Giannarou, S., Mateus, D., Meining, A., Yang, G.Z., Navab, N.: Probabilistic Region Matching in Narrow-Band Endoscopy for Targeted Optical Biopsy. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 499–506. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Atasoy, S., Mateus, D., Lallemand, J., Meining, A., Yang, G.Z., Navab, N.: Endoscopic Video Manifolds. In: Jiang, T., Navab, N., Pluim, J., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 437–445. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Belkin, M., Niyogi, P.: Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Comput. 15(6), 1373–1396 (2003)CrossRefzbMATHGoogle Scholar
  6. 6.
    Davies, D., Bouldin, D.: A Cluster Separation Measure. IEEE Trans. on Pattern Anal. (2), 224–227 (1979)Google Scholar
  7. 7.
    Figueiredo, M., Jain, A.: Unsupervised learning of finite mixture models. IEEE Trans. on Pattern Anal. 24(3), 381–396 (2002)CrossRefGoogle Scholar
  8. 8.
    Hartigan, J., Wong, M.: A k-means clustering algorithm. Journal of the Royal Statistical Society C 28(1), 100–108 (1979)zbMATHGoogle Scholar
  9. 9.
    Häussinger, K., Ballin, A., Becker, H., Bölcskei, P., Dierkesmann, R., Dittrich, I., Frank, W., Freitag, L., Gottschall, R., Guschall, W., Hartmann, W., Hauck, R., Herth, F., Kirsten, D., Kohlhäufl, M., Kreuzer, A., Loddenkemper, R., Macha, N., Markus, A., Stanzel, F., Steffen, H., Wagner, M.: Recommendations for Quality Standards in Bronchoscopy. Pneumologie 58(5), 344 (2004)CrossRefGoogle Scholar
  10. 10.
    He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.: Face Recognition using Laplacianfaces. IEEE Trans. on Pattern Anal. 27(3), 328–340 (2005)CrossRefGoogle Scholar
  11. 11.
    Mountney, P., Giannarou, S., Elson, D., Yang, G.-Z.: Optical Biopsy Mapping for Minimally Invasive Cancer Screening. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 483–490. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Selen Atasoy
    • 1
    • 2
  • Diana Mateus
    • 1
  • Alexander Meining
    • 3
  • Guang-Zhong Yang
    • 2
  • Nassir Navab
    • 1
  1. 1.Chair for Computer Aided Medical Procedures (CAMP)TU MunichGermany
  2. 2.Hamlyn Centre for Robotic SurgeryImperial College LondonUnited Kingdom
  3. 3.Klinikum Rechts der IsarTU MunichGermany

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