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- 저자 : Carina Veil (검색결과 1 건)
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Cystoscopic depth estimation using gated adversarial domain adaptation
Peter Somers,Simon Holdenried-Krafft,Johannes Zahn,Johannes Schüle,Carina Veil,Niklas Harland,Simon Walz,Arnulf Stenzl,Oliver Sawodny,Cristina Tarín,Hendrik P. A. Lensch 대한의용생체공학회 2023 Biomedical Engineering Letters (BMEL) Vol.13 No.2
Monocular depth estimation from camera images is very important for surrounding scene evaluation in many technical fieldsfrom automotive to medicine. However, traditional triangulation methods using stereo cameras or multiple views with theassumption of a rigid environment are not applicable for endoscopic domains. Particularly in cystoscopies it is not possibleto produce ground truth depth information to directly train machine learning algorithms for using a monocular image directlyfor depth prediction. This work considers first creating a synthetic cystoscopic environment for initial encoding of depthinformation from synthetically rendered images. Next, the task of predicting pixel-wise depth values for real images is constrainedto a domain adaption between the synthetic and real image domains. This adaptation is done through added gatedresidual blocks in order to simplify the network task and maintain training stability during adversarial training. Training isdone on an internally collected cystoscopy dataset from human patients. The results after training demonstrate the ability topredict reasonable depth estimations from actual cystoscopic videos and added stability from using gated residual blocks isshown to prevent mode collapse during adversarial training.
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