Jin, W;
Daher, R;
Stoyanov, D;
Vasconcelos, F;
(2023)
A Self-supervised Approach for Detecting the Edges of Haustral Folds in Colonoscopy Video.
In:
Data Engineering in Medical Imaging: First MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings.
(pp. pp. 56-66).
Springer Nature: Cham, Switzerland.
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Abstract
Providing 3D navigation in colonoscopy can help decrease diagnostic miss rates in cancer screening by building a coverage map of the colon as the endoscope navigates the anatomy. However, this task is made challenging by the lack of discriminative localisation landmarks throughout the colon. While standard navigation techniques rely on sparse point landmarks or dense pixel registration, we propose edges as a more natural visual landmark to characterise the haustral folds of the colon anatomy. We propose a self-supervised methodology to train an edge detection method for colonoscopy imaging, demonstrating that it can effectively detect anatomy related edges while ignoring light reflection artifacts abundant in colonoscopy. We also propose a metric to evaluate the temporal consistency of estimated edges in the absence of real groundtruth. We demonstrate our results on video sequences from the public dataset HyperKvazir. Our code and pseudo-groundtruth edge labels are available at https://github.com/jwyhhh123/HaustralFold_Edge_Detector.
| Type: | Proceedings paper |
|---|---|
| Title: | A Self-supervised Approach for Detecting the Edges of Haustral Folds in Colonoscopy Video |
| Event: | MICCAI Workshop on Data Engineering in Medical Imaging |
| ISBN-13: | 978-3-031-44991-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-44992-5_6 |
| Publisher version: | https://doi.org/10.1007/978-3-031-44992-5_6 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Colonoscopy, Scene understanding, Edge detection, Landmark detection |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10181048 |
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