Real-Time Lumen Detection for Autonomous Colonoscopy

Al-Bander, Baidaa; Mathew, Alwyn; Magerand, Ludovic; Trucco, Emanuele; Manfredi, Luigi

Abstract

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.

Citation

Al-Bander, B., Mathew, A., Magerand, L., Trucco, E., & Manfredi, L. (2022, September). Real-Time Lumen Detection for Autonomous Colonoscopy. Presented at First MICCAI Workshop, ISGIE 2022, and Fourth MICCAI Workshop, GRAIL 2022, Held in Conjunction with MICCAI 2022, Singapore

• Presentation Conference Type: Conference Paper (published)
• Conference Name: First MICCAI Workshop, ISGIE 2022, and Fourth MICCAI Workshop, GRAIL 2022, Held in Conjunction with MICCAI 2022
• Start Date: Sep 18, 2022
• End Date: Sep 18, 2022
• Acceptance Date: Dec 10, 2022
• Online Publication Date: Dec 10, 2022
• Publication Date: 2022
• Deposit Date: Jun 2, 2023
• Publisher: Springer
• Pages: 35-44
• Series Title: Lecture Notes in Computer Science
• Series ISSN: 0302-9743
• Book Title: Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis
• ISBN: 978-3-031-21082-2
• DOI: https://doi.org/10.1007/978-3-031-21083-9_4
• Public URL: https://keele-repository.worktribe.com/output/435301
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-031-21083-9_4
• Additional Information: First Online: 10 December 2022; Conference Acronym: ISGIE; Conference Name: MICCAI Workshop on Imaging Systems for GI Endoscopy; Conference City: Singapore; Conference Country: Singapore; Conference Year: 2022; Conference Start Date: 18 September 2022; Conference End Date: 18 September 2022; Conference Number: 1; Conference ID: isgie2022; Type: Double-blind; Conference Management System: CMT; Number of Submissions Sent for Review: 8; Number of Full Papers Accepted: 6; Number of Short Papers Accepted: 0; Acceptance Rate of Full Papers: 75% - The value is computed by the equation "Number of Full Papers Accepted / Number of Submissions Sent for Review * 100" and then rounded to a whole number.; Average Number of Reviews per Paper: 3; Average Number of Papers per Reviewer: 1.3; External Reviewers Involved: Yes