BACKGROUND: The precise segmentation of kidneys and kidney tumors can help medical specialists to diagnose diseases and improve treatment planning, which is highly required in clinical practice. Manual segmentation of the kidneys is extremely time-co...
The international journal of medical robotics + computer assisted surgery : MRCAS
Jun 14, 2020
BACKGROUND: This study assessed the incidence and impact of acute kidney injury (AKI) on renal prognosis in patients who underwent robot-assisted laparoscopic radical prostatectomy (RARP).
PURPOSE: To investigate the effects of different methodologies on the performance of deep learning (DL) model for differentiating high- from low-grade clear cell renal cell carcinoma (ccRCC).
OBJECTIVE: To reliably and quickly diagnose children with posterior urethral valves (PUV), we developed a multi-instance deep learning method to automate image analysis.
Computational and mathematical methods in medicine
May 5, 2020
We propose a new method for fast organ classification and segmentation of abdominal magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is a new type of high-tech imaging examination fashion in recent years. Recognition of specific targe...
We describe Australia's first reported case of robotic kidney autotransplantation for a complex renal artery aneurysm. It is potentially a safe, minimally invasive method of salvaging renal parenchyma and preservation of renal function in patients wi...
Segmentation of normal organs is a critical and time-consuming process in radiotherapy. Auto-segmentation of abdominal organs has been made possible by the advent of the convolutional neural network. We utilized the U-Net, a 3D-patch-based convolutio...
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