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VISUALIZING MISSING SURFACES IN COLONOSCOPY VIDEOS USING SHARED LATENT SPACE REPRESENTATIONS.

Shawn Mathew1, Saad Nadeem2, Arie Kaufman1

  • 1Stony Brook University, Department of Computer Science, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|October 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework to identify missed regions during optical colonoscopy (OC) by leveraging virtual colonoscopy (VC) data. The method enhances colon cancer screening by visualizing potential blind spots in real-time.

Keywords:
ColonoscopyCycleGANDirectional discriminatorShared latent spaceVirtual Colonoscopy

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Gastroenterology

Background:

  • Optical colonoscopy (OC) is the primary method for colon cancer screening but suffers from a high miss rate.
  • Factors contributing to missed regions include colonic geometry, endoscopist limitations, and endoscope field of view.

Purpose of the Study:

  • To develop a framework for visualizing missed regions during OC per-frame.
  • To provide a practical clinical solution for improving colonoscopy miss rates.

Main Methods:

  • Utilized 3D reconstructed virtual colonoscopy (VC) data alongside OC data.
  • Employed a lossy unpaired image-to-image translation model with an enforced shared latent space for OC and VC.
  • Incorporated Gaussian noise input to differentiate geometric information from color, texture, and specular reflections.

Main Results:

  • The shared latent space effectively captures geometric information from both OC and VC.
  • The model can generate one-to-many mappings from VC to OC and OC to OC, aiding in visualization.
  • A framework for real-time visualization of missed regions during OC was developed.

Conclusions:

  • The proposed framework offers a workable clinical solution to address the limitations of OC.
  • By visualizing missed regions, this approach has the potential to significantly improve colon cancer detection rates.
  • The computational framework and associated resources will be publicly released.