%0 Journal Article %1 Somers2023 %A Somers, Peter %A Holdenried-Krafft, Simon %A Zahn, Johannes %A Schüle, Johannes %A Veil, Carina %A Harland, Niklas %A Walz, Simon %A Stenzl, Arnulf %A Sawodny, Oliver %A Tarín, Cristina %A Lensch, Hendrik P. A. %D 2023 %J Biomedical Engineering Letters %K arnulf_stenzl carina_veil cristina_tarin grk2543 hendrik_lensch johannes_schuele journal niklas_harland oliver_sawodny peter_somers reviewed simon_holdenried-krafft simon_walz %N 2 %P 141--151 %R 10.1007/s13534-023-00261-3 %T Cystoscopic depth estimation using gated adversarial domain adaptation %U https://doi.org/10.1007/s13534-023-00261-3 %V 13 %X Monocular depth estimation from camera images is very important for surrounding scene evaluation in many technical fields from automotive to medicine. However, traditional triangulation methods using stereo cameras or multiple views with the assumption of a rigid environment are not applicable for endoscopic domains. Particularly in cystoscopies it is not possible to produce ground truth depth information to directly train machine learning algorithms for using a monocular image directly for depth prediction. This work considers first creating a synthetic cystoscopic environment for initial encoding of depth information from synthetically rendered images. Next, the task of predicting pixel-wise depth values for real images is constrained to a domain adaption between the synthetic and real image domains. This adaptation is done through added gated residual blocks in order to simplify the network task and maintain training stability during adversarial training. Training is done on an internally collected cystoscopy dataset from human patients. The results after training demonstrate the ability to predict reasonable depth estimations from actual cystoscopic videos and added stability from using gated residual blocks is shown to prevent mode collapse during adversarial training.

@article{Somers2023, abstract = {Monocular depth estimation from camera images is very important for surrounding scene evaluation in many technical fields from automotive to medicine. However, traditional triangulation methods using stereo cameras or multiple views with the assumption of a rigid environment are not applicable for endoscopic domains. Particularly in cystoscopies it is not possible to produce ground truth depth information to directly train machine learning algorithms for using a monocular image directly for depth prediction. This work considers first creating a synthetic cystoscopic environment for initial encoding of depth information from synthetically rendered images. Next, the task of predicting pixel-wise depth values for real images is constrained to a domain adaption between the synthetic and real image domains. This adaptation is done through added gated residual blocks in order to simplify the network task and maintain training stability during adversarial training. Training is done on an internally collected cystoscopy dataset from human patients. The results after training demonstrate the ability to predict reasonable depth estimations from actual cystoscopic videos and added stability from using gated residual blocks is shown to prevent mode collapse during adversarial training.}, added-at = {2023-06-26T13:41:37.000+0200}, author = {Somers, Peter and Holdenried-Krafft, Simon and Zahn, Johannes and Sch{\"u}le, Johannes and Veil, Carina and Harland, Niklas and Walz, Simon and Stenzl, Arnulf and Sawodny, Oliver and Tar{\'i}n, Cristina and Lensch, Hendrik P. A.}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2abebb5d790a2518a5efdaae0ea24cb02/ffischer}, day = 01, doi = {10.1007/s13534-023-00261-3}, interhash = {bc0f31cce137a2210a355a96512ca97c}, intrahash = {abebb5d790a2518a5efdaae0ea24cb02}, issn = {2093-985X}, journal = {Biomedical Engineering Letters}, keywords = {arnulf_stenzl carina_veil cristina_tarin grk2543 hendrik_lensch johannes_schuele journal niklas_harland oliver_sawodny peter_somers reviewed simon_holdenried-krafft simon_walz}, month = may, number = 2, pages = {141--151}, timestamp = {2023-08-10T10:14:20.000+0200}, title = {Cystoscopic depth estimation using gated adversarial domain adaptation}, url = {https://doi.org/10.1007/s13534-023-00261-3}, volume = 13, year = 2023 }