3D Reconstruction of virtual colon structures from colonoscopy images

Comput Med Imaging Graph. 2014 Jan;38(1):22-33. doi: 10.1016/j.compmedimag.2013.10.005. Epub 2013 Oct 27.

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).

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

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Algorithms*
  • Colon / diagnostic imaging*
  • Colonography, Computed Tomographic / instrumentation
  • Colonography, Computed Tomographic / methods*
  • Humans
  • Imaging, Three-Dimensional / methods*
  • Pattern Recognition, Automated / methods*
  • Phantoms, Imaging
  • Radiographic Image Enhancement / methods*
  • Radiographic Image Interpretation, Computer-Assisted / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity