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… Click to show full abstract
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. 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. (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. Both the accuracy and stability evaluations demonstrated the feasibility and practicality of our proposed method for achieving 3D annotations.
               
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