Real-time lumen detection for autonomous colonoscopy

Baidaa Al-Bander, Alwyn Mathew, Ludovic Magerand, Manuel Trucco, Luigi Manfredi (Lead / Corresponding author)

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Lumen detection and tracking in the large bowel is a key prerequisite step for autonomous navigation of endorobots for colonoscopy. Attempts at detecting and tracking the lumen so far have been made using optical flow and shape-from-shading techniques. In general, these methods are computationally expensive, and most are either not real-time nor tested on real devices. To this end, we present a deep learning-based approach for lumen localisation from colonoscopy videos. We avoid the need for extensive, costly annotations with a semi-supervised learning and a self-training scheme, whereby only a small subset of video frames is annotated. We develop an end-to-end pseudo-labelling semi-supervised approach incorporating a self-training scheme for colon lumen detection. Our approach reveals a competitive performance to the supervised baseline model with both objective and subjective evaluation metrics, while saving heavy labelling costs in terms of clinicians’ time. Our method for lumen detection runs at 60 ms per frame during the inference phase. Our experiments demonstrate the potential of our system in real-time environments, which contributes towards improving the automation of robotics colonoscopy.
Original languageEnglish
Title of host publicationImaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis
Subtitle of host publicationFirst MICCAI Workshop, ISGIE 2022, and Fourth MICCAI Workshop, GRAIL 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings
EditorsLuigi Manfredi, Seyed-Ahmad Ahmadi, Michael Bronstein, Anees Kazi, Davide Lomanto, Alwyn Mathew, Ludovic Magerand, Kamilia Mullakaeva, Bartlomiej Papiez, Russell H. Taylor, Emanuele Trucco
Number of pages10
ISBN (Electronic)9783031210839
ISBN (Print)9783031210822
Publication statusPublished - 2022

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Autonomous colonoscopy
  • Semi-supervised learning
  • Lumen detection
  • Self-training
  • Endorobots for colonoscopy
  • Bowel cancer

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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