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Article: Real-to-Virtual Domain Transfer-based Depth Estimation for Real-time 3D Annotation in Transnasal Surgery: A Study of Annotation Accuracy and Stability

TitleReal-to-Virtual Domain Transfer-based Depth Estimation for Real-time 3D Annotation in Transnasal Surgery: A Study of Annotation Accuracy and Stability
Authors
KeywordsAugmented reality
Surgical annotation
Monocular depth estimation
Domain transfer learning
Transnasal surgery
Issue Date2021
PublisherSpringer Verlag. The Journal's web site is located at http://www.springer.com/medicine/radiology/journal/11548
Citation
International Journal of Computer Assisted Radiology and Surgery, 2021, v. 16 n. 5, p. 731-739 How to Cite?
AbstractPurpose: Surgical annotation promotes effective communication between medical personnel during surgical procedures. However, existing approaches to 2D annotations are mostly static with respect to a display. In this work, we propose a method to achieve 3D annotations that anchor rigidly and stably to target structures upon camera movement in a transnasal endoscopic surgery setting. Methods: This is accomplished through intra-operative endoscope tracking and monocular depth estimation. A virtual endoscopic environment is utilized to train a supervised depth estimation network. An adversarial network transfers the style from the real endoscopic view to a synthetic-like view for input into the depth estimation network, wherein framewise depth can be obtained in real time. Results: (1) Accuracy: Framewise depth was predicted from images captured from within a nasal airway phantom and compared with ground truth, achieving a SSIM value of 0.8310 ± 0.0655. (2) Stability: mean absolute error (MAE) between reference and predicted depth of a target point was 1.1330 ± 0.9957 mm. Conclusion: Both the accuracy and stability evaluations demonstrated the feasibility and practicality of our proposed method for achieving 3D annotations.
Persistent Identifierhttp://hdl.handle.net/10722/299728
ISSN
2023 Impact Factor: 2.3
2023 SCImago Journal Rankings: 0.853
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorTong, HS-
dc.contributor.authorNg, YL-
dc.contributor.authorLiu, Z-
dc.contributor.authorHo, JDL-
dc.contributor.authorChan, PL-
dc.contributor.authorChan, JYK-
dc.contributor.authorKwok, KW-
dc.date.accessioned2021-05-26T03:28:13Z-
dc.date.available2021-05-26T03:28:13Z-
dc.date.issued2021-
dc.identifier.citationInternational Journal of Computer Assisted Radiology and Surgery, 2021, v. 16 n. 5, p. 731-739-
dc.identifier.issn1861-6410-
dc.identifier.urihttp://hdl.handle.net/10722/299728-
dc.description.abstractPurpose: Surgical annotation promotes effective communication between medical personnel during surgical procedures. However, existing approaches to 2D annotations are mostly static with respect to a display. In this work, we propose a method to achieve 3D annotations that anchor rigidly and stably to target structures upon camera movement in a transnasal endoscopic surgery setting. Methods: This is accomplished through intra-operative endoscope tracking and monocular depth estimation. A virtual endoscopic environment is utilized to train a supervised depth estimation network. An adversarial network transfers the style from the real endoscopic view to a synthetic-like view for input into the depth estimation network, wherein framewise depth can be obtained in real time. Results: (1) Accuracy: Framewise depth was predicted from images captured from within a nasal airway phantom and compared with ground truth, achieving a SSIM value of 0.8310 ± 0.0655. (2) Stability: mean absolute error (MAE) between reference and predicted depth of a target point was 1.1330 ± 0.9957 mm. Conclusion: Both the accuracy and stability evaluations demonstrated the feasibility and practicality of our proposed method for achieving 3D annotations.-
dc.languageeng-
dc.publisherSpringer Verlag. The Journal's web site is located at http://www.springer.com/medicine/radiology/journal/11548-
dc.relation.ispartofInternational Journal of Computer Assisted Radiology and Surgery-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectAugmented reality-
dc.subjectSurgical annotation-
dc.subjectMonocular depth estimation-
dc.subjectDomain transfer learning-
dc.subjectTransnasal surgery-
dc.titleReal-to-Virtual Domain Transfer-based Depth Estimation for Real-time 3D Annotation in Transnasal Surgery: A Study of Annotation Accuracy and Stability-
dc.typeArticle-
dc.identifier.emailHo, JD: jhostaff@hku.hk-
dc.identifier.emailKwok, KW: kwokkw@hku.hk-
dc.identifier.authorityKwok, KW=rp01924-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1007/s11548-021-02346-9-
dc.identifier.pmid33786777-
dc.identifier.pmcidPMC8134290-
dc.identifier.scopuseid_2-s2.0-85103395173-
dc.identifier.hkuros322571-
dc.identifier.hkuros324931-
dc.identifier.volume16-
dc.identifier.issue5-
dc.identifier.spage731-
dc.identifier.epage739-
dc.identifier.isiWOS:000635073000001-
dc.publisher.placeGermany-

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