Literature DB >> 22057050

Endoscopic video manifolds for targeted optical biopsy.

Selen Atasoy1, Diana Mateus, Alexander Meining, Guang-Zhong Yang, Nassir Navab.   

Abstract

Gastro-intestinal (GI) endoscopy is a widely used clinical procedure for screening and surveillance of digestive tract diseases ranging from Barrett's Oesophagus to oesophageal cancer. Current surveillance protocol consists of periodic endoscopic examinations performed in 3-4 month intervals including expert's visual assessment and biopsies taken from suspicious tissue regions. Recent development of a new imaging technology, called probe-based confocal laser endomicroscopy (pCLE), enabled the acquisition of in vivo optical biopsies without removing any tissue sample. Besides its several advantages, i.e., noninvasiveness, real-time and in vivo feedback, optical biopsies involve a new challenge for the endoscopic expert. Due to their noninvasive nature, optical biopsies do not leave any scar on the tissue and therefore recognition of the previous optical biopsy sites in surveillance endoscopy becomes very challenging. In this work, we introduce a clustering and classification framework to facilitate retargeting previous optical biopsy sites in surveillance upper GI-endoscopies. A new representation of endoscopic videos based on manifold learning, "endoscopic video manifolds" (EVMs), is proposed. The low dimensional EVM representation is adapted to facilitate two different clustering tasks; i.e., clustering of informative frames and patient specific endoscopic segments, only by changing the similarity measure. Each step of the proposed framework is validated on three in vivo patient datasets containing 1834, 3445, and 1546 frames, corresponding to endoscopic videos of 73.36, 137.80, and 61.84 s, respectively. Improvements achieved by the introduced EVM representation are demonstrated by quantitative analysis in comparison to the original image representation and principal component analysis. Final experiments evaluating the complete framework demonstrate the feasibility of the proposed method as a promising step for assisting the endoscopic expert in retargeting the optical biopsy sites.

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Year:  2011        PMID: 22057050     DOI: 10.1109/TMI.2011.2174252

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  6 in total

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2.  Classification approach for automatic laparoscopic video database organization.

Authors:  Andru Putra Twinanda; Jacques Marescaux; Michel de Mathelin; Nicolas Padoy
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3.  3D reconstruction of cystoscopy videos for comprehensive bladder records.

Authors:  Kristen L Lurie; Roland Angst; Dimitar V Zlatev; Joseph C Liao; Audrey K Ellerbee Bowden
Journal:  Biomed Opt Express       Date:  2017-03-08       Impact factor: 3.732

4.  Pose estimation of a markerless fiber bundle for endoscopic optical biopsy.

Authors:  Omar Zenteno; Sylvie Treuillet; Yves Lucas
Journal:  J Med Imaging (Bellingham)       Date:  2021-03-01

5.  An image retrieval framework for real-time endoscopic image retargeting.

Authors:  Menglong Ye; Edward Johns; Benjamin Walter; Alexander Meining; Guang-Zhong Yang
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-06-02       Impact factor: 2.924

Review 6.  Is Computer-Assisted Tissue Image Analysis the Future in Minimally Invasive Surgery? A Review on the Current Status of Its Applications.

Authors:  Vasilios Tanos; Marios Neofytou; Ahmed Samy Abdulhady Soliman; Panayiotis Tanos; Constantinos S Pattichis
Journal:  J Clin Med       Date:  2021-12-09       Impact factor: 4.241

  6 in total

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