Urology has always been closely linked to technological progress. In the last few decades, we have witnessed increasing implementation of various technologies and innovations in subdisciplines of urology. While conventional laparoscopy is increasingl...
Supervised learning-based segmentation methods typically require a large number of annotated training data to generalize well at test time. In medical applications, curating such datasets is not a favourable option because acquiring a large number of...
IEEE/ACM transactions on computational biology and bioinformatics
Dec 8, 2020
Histological Gleason grading of tumor patterns is one of the most powerful prognostic predictors in prostate cancer. However, manual analysis and grading performed by pathologists are typically subjective and time-consuming. In this paper, we present...
RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
Nov 19, 2020
PURPOSE: A recently developed deep learning model (U-Net) approximated the clinical performance of radiologists in the prediction of clinically significant prostate cancer (sPC) from prostate MRI. Here, we compare the agreement between lesion segmen...
Convolutional Neural Networks (CNNs) work very well for supervised learning problems when the training dataset is representative of the variations expected to be encountered at test time. In medical image segmentation, this premise is violated when t...
To elucidate factors contributing to early urinary continence recovery after retzius-sparing robot-assisted radical prostatectomy (RS-RARP) by evaluating postoperative pelvic anatomical features between RS-RARP and conventional RARP (CON-RARP). We ...
International journal of urology : official journal of the Japanese Urological Association
Sep 24, 2020
OBJECTIVES: To analyze the correlation between periprostatic fat thickness on multiparametric magnetic resonance imaging and upstaging from cT1/2 to pT3 in robot-assisted radical prostatectomy.
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