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• W3143447746 abstract "Abstract Purpose 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." @default.
• W3143447746 created "2021-04-13" @default.
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• W3143447746 date "2021-03-30" @default.
• W3143447746 modified "2023-10-01" @default.
• W3143447746 title "Real-to-virtual domain transfer-based depth estimation for real-time 3D annotation in transnasal surgery: a study of annotation accuracy and stability" @default.
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• W3143447746 doi "https://doi.org/10.1007/s11548-021-02346-9" @default.
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