Automated classification of urinary stones based on microcomputed tomography images using convolutional neural network.

[1]  Dushyant V. Sahani,et al.  Urinary Stone Detection on CT Images Using Deep Convolutional Neural Networks: Evaluation of Model Performance and Generalization. , 2019, Radiology. Artificial intelligence.

[2]  P. Crispen,et al.  Stone size limits the use of Hounsfield units for prediction of calcium oxalate stone composition. , 2015, Urology.

[3]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[4]  Gary S Collins,et al.  Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration , 2015, Annals of Internal Medicine.

[5]  Victoria Y. Bird,et al.  Prevalence of kidney stones in the USA: The National Health and Nutrition Evaluation Survey , 2018, Journal of Clinical Urology.

[6]  B. Petros,et al.  Kidney Stone Disease: An Update on Current Concepts , 2018, Advances in urology.

[7]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[8]  P. Szewczykowski,et al.  Application of the X-ray micro-computed tomography to the analysis of the structure of polymeric materials , 2019, Polimery.

[9]  M. Zielina,et al.  An analysis of the elemental composition of micro-samples using EDS technique , 2014 .

[10]  Lisa E. Vaughan,et al.  The Changing Incidence and Presentation of Urinary Stones Over 3 Decades , 2018, Mayo Clinic proceedings.

[11]  Brian B. Avants,et al.  The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.

[12]  A. Lambrecht,et al.  Automated analysis of urinary stone composition using Raman spectroscopy: pilot study for the development of a compact portable system for immediate postoperative ex vivo application. , 2013, The Journal of urology.

[13]  Hayit Greenspan,et al.  Chest pathology detection using deep learning with non-medical training , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[14]  K. Rafaels,et al.  Structural analysis of the frontal and parietal bones of the human skull. , 2019, Journal of the mechanical behavior of biomedical materials.

[15]  Hoo-Chang Hoo-Chang Shin Shin,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, Ieee Transactions on Medical Imaging.

[16]  P. Rajesh Kumar,et al.  Detection of Chronic Kidney Disease by Using Artificial Neural Networks and Gravitational Search Algorithm , 2018, Lecture Notes in Networks and Systems.

[17]  D. Moon,et al.  Computed Tomography-Based Novel Prediction Model for the Outcome of Shockwave Lithotripsy in Proximal Ureteral Stones. , 2016, Journal of endourology.

[18]  W. Roberts,et al.  Deep learning computer vision algorithm for detecting kidney stone composition: Towards an automated future , 2019, European Urology Supplements.

[19]  Max A. Viergever,et al.  Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks , 2016, Medical Image Anal..

[20]  F. Haryanto,et al.  Optimization of Imaging Parameters in Micro-CT Scanner Based On Signal-To-Noise Ratio for the Analysis of Urinary Stone Composition , 2020 .

[21]  C. Barbas,et al.  Urinary analysis of nephrolithiasis markers. , 2002, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[22]  Toniann Pitassi,et al.  Generalization in Adaptive Data Analysis and Holdout Reuse , 2015, NIPS.

[23]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[24]  Seyed Abolghasem Mirroshandel,et al.  A novel method for predicting kidney stone type using ensemble learning , 2017, Artif. Intell. Medicine.

[25]  W. Roberts,et al.  PD04-06 DEEP LEARNING COMPUTER VISION ALGORITHM FOR DETECTING KIDNEY STONE COMPOSITION: TOWARDS AN AUTOMATED FUTURE , 2019, Journal of Urology.

[26]  John M. Martinis,et al.  High‐resolution, energy‐dispersive microcalorimeter spectrometer for X‐ray microanalysis , 1997 .

[27]  Qiaoliang Li,et al.  Urine Calcium Oxalate Crystallization Recognition Method Based on Deep Learning , 2019, 2019 International Conference on Automation, Computational and Technology Management (ICACTM).

[28]  Max Kuhn,et al.  Applied Predictive Modeling , 2013 .

[29]  Alvin C. Silva,et al.  Dual-energy vs conventional computed tomography in determining stone composition. , 2014, Urology.

[30]  M. Lidén A new method for predicting uric acid composition in urinary stones using routine single-energy CT , 2017, Urolithiasis.

[31]  Min Hyoung Cho,et al.  A flat-panel detector based micro-CT system: performance evaluation for small-animal imaging. , 2003, Physics in medicine and biology.

[32]  Amy Loutfi,et al.  Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks , 2018, Comput. Biol. Medicine.

[33]  Steve P Martin,et al.  Differentiating kidney stones from phleboliths in unenhanced low-dose computed tomography using radiomics and machine learning , 2019, European Radiology.

[34]  C. Iselin,et al.  Low-dose versus standard-dose CT protocol in patients with clinically suspected renal colic. , 2007, AJR. American journal of roentgenology.

[35]  Saritha Ranabothu,et al.  Diagnosis and management of non-calcium-containing stones in the pediatric population , 2018, International Urology and Nephrology.

[36]  Wei Shen,et al.  Multi-scale Convolutional Neural Networks for Lung Nodule Classification , 2015, IPMI.

[37]  M. Pearle,et al.  Dual-layer spectral detector CT: non-inferiority assessment compared to dual-source dual-energy CT in discriminating uric acid from non-uric acid renal stones ex vivo , 2018, Abdominal Radiology.