Literature DB >> 24225230

3D Reconstruction of virtual colon structures from colonoscopy images.

DongHo Hong1, Wallapak Tavanapong2, Johnny Wong3, JungHwan Oh4, Piet C de Groen5.   

Abstract

This paper presents the first fully automated reconstruction technique of 3D virtual colon segments from individual colonoscopy images. It is the basis of new software applications that may offer great benefits for improving quality of care for colonoscopy patients. For example, a 3D map of the areas inspected and uninspected during colonoscopy can be shown on request of the endoscopist during the procedure. The endoscopist may revisit the suggested uninspected areas to reduce the chance of missing polyps that reside in these areas. The percentage of the colon surface seen by the endoscopist can be used as a coarse objective indicator of the quality of the procedure. The derived virtual colon models can be stored for post-procedure training of new endoscopists to teach navigation techniques that result in a higher level of procedure quality. Our technique does not require a prior CT scan of the colon or any global positioning device. Our experiments on endoscopy images of an Olympus synthetic colon model reveal encouraging results with small average reconstruction errors (4.1 mm for the fold depths and 12.1 mm for the fold circumferences).
Copyright © 2013 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  3D Colon reconstruction; Optical colonoscopy; Quality of colonoscopy; Virtual colon

Mesh:

Year:  2013        PMID: 24225230     DOI: 10.1016/j.compmedimag.2013.10.005

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  6 in total

1.  Automated visibility map of the internal colon surface from colonoscopy video.

Authors:  Mohammad Ali Armin; Girija Chetty; Hans De Visser; Cedric Dumas; Florian Grimpen; Olivier Salvado
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-08-04       Impact factor: 2.924

Review 2.  Quality indicators for colonoscopy: Current insights and caveats.

Authors:  Hendrikus Jm Pullens; Peter D Siersema
Journal:  World J Gastrointest Endosc       Date:  2014-12-16

Review 3.  Flexible Gastro-intestinal Endoscopy - Clinical Challenges and Technical Achievements.

Authors:  Niehls Kurniawan; Martin Keuchel
Journal:  Comput Struct Biotechnol J       Date:  2017-01-18       Impact factor: 7.271

4.  Implicit domain adaptation with conditional generative adversarial networks for depth prediction in endoscopy.

Authors:  Anita Rau; P J Eddie Edwards; Omer F Ahmad; Paul Riordan; Mirek Janatka; Laurence B Lovat; Danail Stoyanov
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-04-15       Impact factor: 2.924

5.  Learning colon centreline from optical colonoscopy, a new way to generate a map of the internal colon surface.

Authors:  Mohammad Ali Armin; Nick Barnes; Florian Grimpen; Olivier Salvado
Journal:  Healthc Technol Lett       Date:  2019-11-26

6.  Artificial Intelligence for Colonoscopy: Past, Present, and Future.

Authors:  Wallapak Tavanapong; JungHwan Oh; Michael A Riegler; Mohammed Khaleel; Bhuvan Mittal; Piet C de Groen
Journal:  IEEE J Biomed Health Inform       Date:  2022-08-11       Impact factor: 7.021

  6 in total

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