A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans
Published 2022 View Full Article
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Title
A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans
Authors
Keywords
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Journal
MEDICAL PHYSICS
Volume -, Issue -, Pages -
Publisher
Wiley
Online
2022-02-14
DOI
10.1002/mp.15518
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Note: Only part of the references are listed.- Radiation Dose Reduction in Kidney Stone CT: A Randomized, Facility-Based Intervention
- (2021) Christopher L. Moore et al. Journal of the American College of Radiology
- Atherosclerotic Plaque Burden on Abdominal CT: Automated Assessment With Deep Learning on Noncontrast and Contrast-enhanced Scans
- (2020) Ronald M. Summers et al. ACADEMIC RADIOLOGY
- Comparing different kidney stone scoring systems for predicting percutaneous nephrolithotomy outcomes: A multicenter retrospective cohort study
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- Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study
- (2020) Fatemeh Homayounieh et al. Abdominal Radiology
- In-vitro comparison of different slice thicknesses and kernel settings for measurement of urinary stone size by computed tomography
- (2019) Roland Umbach et al. Urolithiasis
- Computed tomography window affects kidney stones measurements
- (2019) Alexandre Danilovic et al. International Braz J Urol
- Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks
- (2019) Veit Sandfort et al. Scientific Reports
- Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks
- (2018) Martin Längkvist et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Prediction of spontaneous ureteral stone passage: Automated 3D-measurements perform equal to radiologists, and linear measurements equal to volumetric
- (2018) Johan Jendeberg et al. EUROPEAN RADIOLOGY
- A User-Friendly Application to Automate CT Renal Stone Measurement
- (2018) Justin B. Ziemba et al. JOURNAL OF ENDOUROLOGY
- Ultra-low-dose limited renal CT for volumetric stone surveillance: advantages over standard unenhanced CT
- (2018) Virginia B. Planz et al. Abdominal Radiology
- Emergency Department Revisits for Patients with Kidney Stones in California
- (2015) Charles D. Scales et al. ACADEMIC EMERGENCY MEDICINE
- Quantification of Asymptomatic Kidney Stone Burden by Computed Tomography for Predicting Future Symptomatic Stone Events
- (2015) Michael G. Selby et al. UROLOGY
- Computer-aided detection of renal calculi from noncontrast CT images using TV-flow and MSER features
- (2014) Jianfei Liu et al. MEDICAL PHYSICS
- Emergency department visits, use of imaging, and drugs for urolithiasis have increased in the United States
- (2013) Chyng-Wen Fwu et al. KIDNEY INTERNATIONAL
- Prevalence of Kidney Stones in the United States
- (2012) Charles D. Scales et al. EUROPEAN UROLOGY
- Automated Volumetric Assessment by Noncontrast Computed Tomography in the Surveillance of Nephrolithiasis
- (2012) Sutchin R. Patel et al. UROLOGY
- Dietary therapy for patients with hypocitraturic nephrolithiasis
- (2011) Michael P. Kurtz et al. Nature Reviews Urology
- Quantification of Urinary Stone Volume: Attenuation Threshold–based CT Method—A Technical Note
- (2011) Shadpour Demehri et al. RADIOLOGY
- New and Evolving Concepts in the Imaging and Management of Urolithiasis: Urologists’ Perspective
- (2010) Avinash R. Kambadakone et al. RADIOGRAPHICS
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