Pan, Yirou;
Bano, Sophia;
Vasconcelos, Francisco;
Park, Hyun;
Ted. Jeong, Taikyeong;
Stoyanov, Danail;
(2022)
DeSmoke-LAP: improved unpaired image-to-image translation for desmoking in laparoscopic surgery.
International Journal of Computer Assisted Radiology and Surgery
10.1007/s11548-022-02595-2.
(In press).
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Abstract
Purpose Robotic-assisted laparoscopic surgery has become the trend in medicine thanks to its convenience and lower risk of infection against traditional open surgery. However, the visibility during these procedures may severely deteriorate due to electrocauterisation which generates smoke in the operating cavity. This decreased visibility hinders the procedural time and surgical performance. Recent deep learning-based techniques have shown the potential for smoke and glare removal, but few targets laparoscopic videos. Method We propose DeSmoke-LAP, a new method for removing smoke from real robotic laparoscopic hysterectomy videos. The proposed method is based on the unpaired image-to-image cycle-consistent generative adversarial network in which two novel loss functions, namely, inter-channel discrepancies and dark channel prior, are integrated to facilitate smoke removal while maintaining the true semantics and illumination of the scene. Results DeSmoke-LAP is compared with several state-of-the-art desmoking methods qualitatively and quantitatively using referenceless image quality metrics on 10 laparoscopic hysterectomy videos through 5-fold cross-validation. Conclusion DeSmoke-LAP outperformed existing methods and generated smoke-free images without applying ground truths (paired images) and atmospheric scattering model. This shows distinctive achievement in dehazing in surgery, even in scenarios with partial inhomogenenous smoke. Our code and hysterectomy dataset will be made publicly available at https://www.ucl.ac.uk/interventional-surgical-sciences/weiss-open-research/weiss-open-data-server/desmoke-lap.
| Type: | Article |
|---|---|
| Title: | DeSmoke-LAP: improved unpaired image-to-image translation for desmoking in laparoscopic surgery |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s11548-022-02595-2 |
| Publisher version: | http://doi.org/10.1007/s11548-022-02595-2 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Desmoking, Robotic-assisted laparoscopic hysterectomy, Deep learning, Generative adversarial network |
| UCL classification: | 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 UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10146417 |
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