One step surgical scene restoration for robot assisted minimally invasive surgery
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Description
Minimally invasive surgery (MIS) offers several advantages to patients including minimum blood loss and quick recovery time. However, lack of tactile or haptic feedback and poor visualization of the surgical site often result in some unintentional tissue damage. Visualization aspects further limits the collection of imaged frame contextual details, therefore the utility of computational methods such as tracking of tissue and tools, scene segmentation, and depth estimation are of paramount interest. Here, we discuss an online preprocessing framework that overcomes routinely encountered visualization challenges associated with the MIS. We resolve three pivotal surgical scene reconstruction tasks in a single step; namely, (i) denoise, (ii) deblur, and (iii) color correction. Our proposed method provides a latent clean and sharp image in the standard RGB color space from its noisy, blurred, and raw inputs in a single preprocessing step (end-to-end in one step). The proposed approach is compared against current state-of-the-art methods that perform each of the image restoration tasks separately. Results from knee arthroscopy show that our method outperforms existing solutions in tackling high-level vision tasks at a significantly reduced computation time.
Impact and interest:
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ID Code: | 238234 | ||||||
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Item Type: | Contribution to Journal (Journal Article) | ||||||
Refereed: | Yes | ||||||
ORCID iD: |
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Additional Information: | Acknowledgements: This work is supported by Australian Indian Strategic Research Fund Project, grant number AISRF53820 and QUT’s Medical and Engineering Research Facility (MERF). Dataset presented in this study was collected from cadaveric subjects at MERF from 2017–2019 with ethics approval no. 1400000856, title -A cadaveric study of optimal leg placement and image capture for potential robotic knee arthroscopy. All experimental protocols were approved by a QUT’s Human Research Ethics committee and research methods were carried out in accordance with guidelines and regulations with approvals obtained from legally authorized representatives at MERF. | ||||||
Measurements or Duration: | 11 pages | ||||||
DOI: | 10.1038/s41598-022-26647-4 | ||||||
ISSN: | 2045-2322 | ||||||
Pure ID: | 126130129 | ||||||
Divisions: | Current > Research Centres > Centre for Materials Science Current > Research Centres > Centre for Biomedical Technologies Current > QUT Faculties and Divisions > Faculty of Science Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Electrical Engineering & Robotics Current > Schools > School of Mechanical, Medical & Process Engineering Current > QUT Faculties and Divisions > Faculty of Health Current > Schools > School of Clinical Sciences |
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Copyright Owner: | The Author(s) 2023 | ||||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||||
Deposited On: | 28 Feb 2023 01:59 | ||||||
Last Modified: | 03 Apr 2024 11:17 |
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