The International Radiomics Platform – An Initiative of the German and Austrian Radiological Societies – First Application Examples Die Internationale Radiomics-Plattform – eine Initiative der Deutschen und Österreichischen Röntgengesellschaften – Erste Anwendungsbeispiele
暂无分享,去创建一个
Jan-Martin Kuhnigk | Matthias Gutberlet | Sergios Gatidis | Peter Kohlmann | Daniel Overhoff | Alex Frydrychowicz | Christian Loewe | Jan Moltz | H Winter | Martin Völker | Horst Hahn | Stefan O Schoenberg | A. Frydrychowicz | J. Moltz | J. Kuhnigk | G. Antoch | D. Overhoff | S. Gatidis | M. Forsting | H. Prosch | A. Dörfler | Horst Hahn | J. Furtner-Srajer | D. Pinto dos Santos | G. Langs | E. Sorantin | F. Körber | E. Gizewski | J. Lotz | M. Gutberlet | C. Loewe | J. Wessling | R. Forstner | P. Kohlmann | M. Gutberlet | F. Anton | S. Lohwasser | H. Winther | J. Barkhausen | K. Hausegger | M. Völker | B. Krause | G. Layer | M. Wucherer | J. Kather | B. Baessler | H. Winter | G. Krombach | S. Schoenberg | U. Attenberger | F. Bamberg | H. Hahn | C. Herold | A. Kaindl | J. Kather | R. Kikinis | P. Kohlmann | M. Ladd | M. Zimmermann | S. Neumann | G. Heinz | K. Wicke | J. Furtner-Srajer
[1] Rob J van der Geest,et al. Automated left ventricle segmentation in late gadolinium‐enhanced MRI for objective myocardial scar assessment , 2015, Journal of magnetic resonance imaging : JMRI.
[2] R. Steenbakkers,et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. , 2020, Radiology.
[3] Fatemeh Zabihollahy,et al. Convolutional neural network‐based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images , 2019, Medical physics.
[4] Hamid Jafarkhani,et al. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI , 2015, Medical Image Anal..
[5] Horst K. Hahn,et al. Radiomics & Deep Learning: Quo vadis? , 2020, Forum.
[6] Ron Kikinis,et al. Statistical validation of image segmentation quality based on a spatial overlap index. , 2004, Academic radiology.
[7] Xin Yang,et al. Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? , 2018, IEEE Transactions on Medical Imaging.
[8] Raymond H Mak,et al. Radiomic phenotype features predict pathological response in non-small cell lung cancer. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[9] C. Langlotz,et al. A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop. , 2019, Radiology.
[10] Barbara Prainsack,et al. Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations , 2020, European Radiology.
[11] Wieslaw Lucjan Nowinski,et al. An Image-Based Comprehensive Approach for Automatic Segmentation of Left Ventricle from Cardiac Short Axis Cine MR Images , 2011, Journal of Digital Imaging.
[12] P. Lambin,et al. Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.
[13] Matthias Gutberlet,et al. Comprehensive Cardiac Magnetic Resonance Imaging in Patients With Suspected Myocarditis: The MyoRacer-Trial. , 2016, Journal of the American College of Cardiology.
[14] Prateek Prasanna,et al. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in Stage I, II resectable Non-Small Cell Lung Cancer: a retrospective multi-cohort study for outcome prediction. , 2020, The Lancet. Digital health.
[15] D. Pennell,et al. Normalized left ventricular systolic and diastolic function by steady state free precession cardiovascular magnetic resonance. , 2006, Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance.
[16] Daniel Schönberger,et al. Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications , 2019, Int. J. Law Inf. Technol..
[17] Paul Kinahan,et al. Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.
[18] James C Moon,et al. Interstudy reproducibility of right ventricular volumes, function, and mass with cardiovascular magnetic resonance. , 2004, American heart journal.
[19] P. Lambin,et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. , 2015, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[20] Andriy Fedorov,et al. Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.
[21] J. E. van Timmeren,et al. Tracking tumor biology with radiomics: A systematic review utilizing a radiomics quality score. , 2018, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[22] Yi Wang,et al. Left ventricle: automated segmentation by using myocardial effusion threshold reduction and intravoxel computation at MR imaging. , 2008, Radiology.
[23] Matthias Gutberlet,et al. Cardiac MRI and Texture Analysis of Myocardial T1 and T2 Maps in Myocarditis with Acute versus Chronic Symptoms of Heart Failure. , 2019, Radiology.
[24] Xiangyang Xu,et al. Automatic left ventricle segmentation in cardiac MRI using topological stable-state thresholding and region restricted dynamic programming. , 2012, Academic radiology.
[25] Matthias Gutberlet,et al. Cardiovascular Magnetic Resonance in Nonischemic Myocardial Inflammation: Expert Recommendations. , 2018, Journal of the American College of Cardiology.
[26] Georg Mühlenbruch,et al. Automated vs. manual assessment of left ventricular function in cardiac multidetector row computed tomography: comparison with magnetic resonance imaging , 2006, European Radiology.
[27] B. Baessler,et al. Robustness and Reproducibility of Radiomics in Magnetic Resonance Imaging: A Phantom Study , 2019, Investigative radiology.
[28] Gustavo Carneiro,et al. Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance , 2017, Medical Image Anal..