Liu, T;
Li, H;
Bai, L;
Wu, Y;
Wang, A;
Islam, M;
Ren, H;
(2023)
Landmark Detection using Transformer Toward Robot-assisted Nasal Airway Intubation.
In:
Procedia Computer Science: Proceedings of International Conference on Biomimetic Intelligence and Robotics.
(pp. pp. 36-42).
Elsevier BV
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Abstract
Robot-assisted airway intubation application needs high accuracy in locating targets and organs. Two vital landmarks, nostrils and glottis, can be detected during the intubation to accommodate the stages of nasal intubation. Automated landmark detection can provide accurate localization and quantitative evaluation. The Detection Transformer (DeTR) leads object detectors to a new paradigm with long-range dependence. However, current DeTR requires long iterations to converge, and does not perform well in detecting small objects. This paper proposes a transformer-based landmark detection solution with deformable DeTR and the semantic-aligned-matching module for detecting landmarks in robot-assisted intubation. The semantics aligner can effectively align the semantics of object queries and image features in the same embedding space using the most discriminative features. To evaluate the performance of our solution, we utilize a publicly accessible glottis dataset and automatically annotate a nostril detection dataset. The experimental results demonstrate our competitive performance in detection accuracy. Our code can be accessible at https://github.com/ConorLTH/airway intubation landmarks detection.
| Type: | Proceedings paper |
|---|---|
| Title: | Landmark Detection using Transformer Toward Robot-assisted Nasal Airway Intubation |
| Event: | International Conference on Biomimetic Intelligence and Robotics |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.procs.2023.10.633 |
| Publisher version: | https://doi.org/10.1016/j.procs.2023.10.633 |
| Language: | English |
| Additional information: | © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Proceedings of International Conference on Biomimetic Intelligence and Robots |
| Keywords: | Bleeding regions segmentation; Medical image segmentation; Semi-supervised learning; Video capsule endoscopy |
| 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 Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10187450 |
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