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RISS 인기검색어
- 검색키워드
- 저자 : Simon Walz (검색결과 2 건)
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Bone Health Issues in Patients with Prostate Cancer: An Evidence-Based Review
Simon Walz,Moritz Maas,Arnulf Stenzl,Tilman Todenhöfer 대한남성과학회 2020 The World Journal of Men's Health Vol.38 No.2
Bone health in prostate cancer patients represents a prerequisite for acceptable quality of life and optimal outcome of this disease. The major threat for bone health in prostate cancer displays cancer treatment induced bone loss as well as the development of bone metastases. In recent years, several new pharmaceuticals targeting bone metabolism such as denosumab or androgen pathway targeting drugs (abiraterone acetate and enzalutamide) have been approved for the treatment of progressive disease aiming to interrupt the vicious circle of bone metastasis and aberrant bone resorption. This development raised the awareness of the pivotal role of bone health in prostate cancer and introduced (symptomatic) skeletal related events as an important end point in recent clinical trials. Bone targeted drugs have become standard of care in patients with metastatic castration resistant prostate cancer, their role in metastatic hormone sensitive prostate cancer has been discussed controversely. In oligometastatic prostate cancer patients several promising approaches in metastasis directed therapy, including conventional surgery, stereotactic ablative radiation and image-guided single-fraction robotic stereotactic radiosurgery (CyberKnife®) were launched but are not in routine clinical use until now caused by sparse clinical evidence.
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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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