Tissue-border detection in volumetric laser endomicroscopy using bi-directional gated recurrent neural networks

Sanne E. Okel, Fons van der Sommen, Endi Selmanaj, Joost van der Putten, Maarten R. Struyvenberg, Jacques J.G.H.M. Bergman, Peter H.N. De With

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Computer-aided detection (CAD) approaches have shown promising results for early esophageal cancer detection using Volumetric Laser Endoscopy (VLE) imagery. However, the relatively slow and computationally costly tissue segmentation employed in these approaches hamper their clinical applicability. In this paper, we propose to reframe the 2D tissue segmentation problem into a 1D tissue boundary detection problem. Instead of using an encoder-decoder architecture, we propose to follow the tissue boundary using a Recurrent Neural Network (RNN), exploiting the spatio-temporal relations within VLE frames. We demonstrate a near state-of-the-art performance using 18 times less floating point operations, enabling real-time execution in clinical practice.

Original languageEnglish
Title of host publicationMedical Imaging 2021
Subtitle of host publicationComputer-Aided Diagnosis
EditorsMaciej A. Mazurowski, Karen Drukker
Number of pages9
ISBN (Electronic)9781510640238
Publication statusPublished - 2021
EventSPIE Medical Imaging 2021 - Online, United States
Duration: 15 Feb 202119 Feb 2021

Publication series

NameProceedings of SPIE
ISSN (Print)1605-7422


ConferenceSPIE Medical Imaging 2021
Country/TerritoryUnited States


  • Barrett's esophagus
  • Computer aided detection
  • Deep learning
  • Recurrent neural network
  • Volumetric laser endomicroscopy


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