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RISS 인기검색어
- 검색키워드
- 저자 : Niklas Harland (검색결과 2 건)
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Harland Niklas,Stenzl Arnulf,Todenhöfer Tilman 대한남성과학회 2021 The World Journal of Men's Health Vol.39 No.1
Multiparametric magnetic resonance imaging (mpMRI) and the introduction of standardized protocols for its interpretation have had a significant impact on the field of prostate cancer (PC). Multiple randomized controlled trials have shown that the sensitivity for detection of clinically significant PC is increased when mpMRI results are the basis for indication of a prostate biopsy. The added value with regards to sensitivity has been strongest for patients with persistent suspicion for PC after a prior negative biopsy. Although enhanced sensitivity of mpMRI is convincing, studies that have compared mpMRI with prostatectomy specimens prepared by whole-mount section analysis have shown a significant number of lesions that were not detected by mpMRI. In this context, the importance of an additional systematic biopsy (SB) is still being debated. While SB in combination with targeted biopsies leads to an increased detection rate, most of the tumors detected by SB only are considered clinically insignificant. Currently, multiple risk calculation tools are being developed that include not only clinical parameters but mpMRI results in addition to clinical parameters in order to improve risk stratification for PC, such as the Partin tables. In summary, mpMRI of the prostate has become a standard procedure recommended by multiple important guidelines for the diagnostic work-up of patients with suspicion of PC.
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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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