Owen, D;
Grammatikopoulou, M;
Luengo, I;
Stoyanov, D;
(2021)
Detection of Critical Structures in Laparoscopic Cholecystectomy Using Label Relaxation and Self-supervision.
In: DeBruijne, M and Cattin, PC and Cotin, S and Padoy, N and Speidel, S and Zheng, Y and Essert, C, (eds.)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021.
(pp. pp. 321-330).
Springer Nature: Cham, Switzerland.
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Detection of Critical Structures in Laparoscopic Cholecystectomy Using Label Relaxation and Self-supervision - Manuscript.pdf - Accepted Version Download (6MB) | Preview |
Abstract
Laparoscopic cholecystectomy can be subject to complications such as bile duct injury, which can seriously harm the patient or even result in death. Computer-assisted interventions have the potential to prevent such complications by highlighting the critical structures (cystic duct and cystic artery) during surgery, helping the surgeon establish the Critical View of Safety and avoid structure misidentification. A method is presented to detect the critical structures, using state of the art computer vision techniques. The proposed label relaxation dramatically improves performance for segmenting critical structures, which have ambiguous extent and highly variable ground truth labels. We also demonstrate how pseudo-label self-supervision allows further detection improvement using unlabelled data. The system was trained using a dataset of 3,050 labelled and 3,682 unlabelled laparoscopic cholecystectomy frames. We achieved an IoU of .65 and presence detection F1 score of .75. The model’s outputs were further evaluated qualitatively by three expert surgeons, providing preliminary confirmation of our method’s benefits. This work is among the first to perform detection of critical anatomy during laparoscopic cholecystectomy, and demonstrates the great promise of computer-assisted intervention to improve surgical safety and workflow.
| Type: | Proceedings paper |
|---|---|
| Title: | Detection of Critical Structures in Laparoscopic Cholecystectomy Using Label Relaxation and Self-supervision |
| Event: | 24th International Conference on Medical Image Computing and Computer-Assisted Intervention |
| Location: | Strasbourg, France |
| Dates: | 27th September 2021 - 1st October 2021 |
| ISBN-13: | 978-3-030-87201-4 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-87202-1_31 |
| Publisher version: | https://doi.org/10.1007/978-3-030-87202-1_31 |
| 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: | Surgical video, Anatomy detection, Self-supervised learning |
| 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/10141260 |
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