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Pathological Site Retargeting under Tissue Deformation Using Geometrical Association and Tracking

  • Menglong Ye
  • Stamatia Giannarou
  • Nisha Patel
  • Julian Teare
  • Guang-Zhong Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

Abstract

Recent advances in microscopic detection techniques include fluorescence spectroscopy, fibred confocal microscopy and optical coherence tomography. These methods can be integrated with miniaturised probes to assist endoscopy, thus enabling diseases to be detected at an early and pre-invasive stage, forgoing the need for histopathological samples and off-line analysis. Since optical-based biopsy does not leave visible marks after sampling, it is important to track the biopsy sites to enable accurate retargeting and subsequent serial examination. In this paper, a novel approach is proposed for pathological site retargeting in gastroscopic examinations. The proposed method is based on affine deformation modelling with geometrical association combined with cascaded online learning and tracking. It provides online in vivo retargeting, and is able to track pathological sites in the presence of tissue deformation. It is also robust to partial occlusions and can be applied to a range of imaging probes including confocal laser endomicroscopy.

Keywords

Narrow Band Image Surface Patch Partial Occlusion Biopsy Site Tissue Deformation 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Menglong Ye
    • 1
  • Stamatia Giannarou
    • 1
  • Nisha Patel
    • 1
    • 2
  • Julian Teare
    • 2
  • Guang-Zhong Yang
    • 1
  1. 1.The Hamlyn Centre for Robotic SurgeryImperial College LondonUK
  2. 2.Department of Surgery and CancerImperial College LondonUK

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