Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping
暂无分享,去创建一个
Stefan K. Piechnik | Stefan Neubauer | Qiang Zhang | Evan Hann | Ricardo A. Gonzales | Iulia A. Popescu | Ahmet Barutcu | Vanessa M. Ferreira | S. Piechnik | S. Neubauer | Qiang Zhang | A. Barutcu | V. Ferreira | R. A. Gonzales | E. Hann
[1] Richard B. Thompson,et al. Clinical recommendations for cardiovascular magnetic resonance mapping of T1, T2, T2* and extracellular volume: A consensus statement by the Society for Cardiovascular Magnetic Resonance (SCMR) endorsed by the European Association for Cardiovascular Imaging (EACVI) , 2017, Journal of Cardiovascular Magnetic Resonance.
[2] Ender Konukoglu,et al. PHiSeg: Capturing Uncertainty in Medical Image Segmentation , 2019, MICCAI.
[3] Michael Brady,et al. Deep Quantitative Liver Segmentation and Vessel Exclusion to Assist in Liver Assessment , 2017, MIUA.
[4] van Hc Hans Assen,et al. LV Challenge LKEB Contribution: Fully Automated Myocardial Contour Detection , 2009, The MIDAS Journal.
[5] Jeonghwan Gwak,et al. Ensemble of Instance Segmentation Models for Polyp Segmentation in Colonoscopy Images , 2019, IEEE Access.
[6] Lin Yang,et al. A New Ensemble Learning Framework for 3D Biomedical Image Segmentation , 2018, AAAI.
[7] Klaus H. Maier-Hein,et al. A Probabilistic U-Net for Segmentation of Ambiguous Images , 2018, NeurIPS.
[8] Stefan Neubauer,et al. Myocardial Tissue Characterization by Magnetic Resonance Imaging , 2014, Journal of thoracic imaging.
[9] Andrew S Flett,et al. Human non-contrast T1 values and correlation with histology in diffuse fibrosis , 2013, Heart.
[10] Reza Nezafat,et al. Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks , 2019, Journal of Cardiovascular Magnetic Resonance.
[11] Ben Glocker,et al. Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI , 2019, American Journal of Neuroradiology.
[12] William T. Clarke,et al. Relationship Between Left Ventricular Structural and Metabolic Remodeling in Type 2 Diabetes , 2015, Diabetes.
[13] Looi Chow Lee,et al. Segmentation of the Left Ventricle from Cine MR Images Using a Comprehensive Approach , 2009, The MIDAS Journal.
[14] Konrad Werys,et al. Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging , 2019, MICCAI.
[15] Ben Glocker,et al. Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study , 2019, Journal of Cardiovascular Magnetic Resonance.
[16] Suyash P. Awate,et al. A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration , 2019, IPMI.
[17] J. Zamorano,et al. Innovative imaging methods in heart failure: a shifting paradigm in cardiac assessment. Position statement on behalf of the Heart Failure Association of the European Society of Cardiology , 2018, European journal of heart failure.
[18] M. Robson,et al. Pheochromocytoma Is Characterized by Catecholamine-Mediated Myocarditis, Focal and Diffuse Myocardial Fibrosis, and Myocardial Dysfunction. , 2016, Journal of the American College of Cardiology.
[19] S. Piechnik,et al. HIV-1–Related Cardiovascular Disease Is Associated With Chronic Inflammation, Frequent Pericardial Effusions, and Probable Myocardial Edema , 2016, Circulation. Cardiovascular imaging.
[20] Stefan Neubauer,et al. Shortened Modified Look-Locker Inversion recovery (ShMOLLI) for clinical myocardial T1-mapping at 1.5 and 3 T within a 9 heartbeat breathhold , 2010, Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance.
[21] Timo Kohlberger,et al. Evaluating Segmentation Error without Ground Truth , 2012, MICCAI.
[22] P. Matthews,et al. Diffuse Myocardial Fibrosis and Inflammation in Rheumatoid Arthritis: Insights From CMR T1 Mapping. , 2015, JACC. Cardiovascular imaging.
[23] G. Wright,et al. Evaluation Framework for Algorithms Segmenting Short Axis Cardiac MRI. , 2009, The MIDAS Journal.
[24] J. Francis,et al. Adenosine stress native T1 mapping in severe aortic stenosis: evidence for a role of the intravascular compartment on myocardial T1 values , 2014, Journal of Cardiovascular Magnetic Resonance.
[25] William M. Wells,et al. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.
[26] M. Robson,et al. Noncontrast T1 mapping for the diagnosis of cardiac amyloidosis. , 2013, JACC. Cardiovascular imaging.
[27] Ben Glocker,et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks , 2017, Journal of Cardiovascular Magnetic Resonance.
[28] M. Robson,et al. Native T1-mapping detects the location, extent and patterns of acute myocarditis without the need for gadolinium contrast agents , 2014, Journal of Cardiovascular Magnetic Resonance.
[29] Ling Shao,et al. A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging , 2016, Magnetic Resonance Materials in Physics, Biology and Medicine.
[30] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[31] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[32] N. Geller,et al. Hypertrophic Cardiomyopathy Registry: The rationale and design of an international, observational study of hypertrophic cardiomyopathy. , 2015, American heart journal.
[33] Ben Glocker,et al. Subject-level Prediction of Segmentation Failure using Real-Time Convolutional Neural Nets , 2018 .
[34] P. Matthews,et al. Subclinical myocardial inflammation and diffuse fibrosis are common in systemic sclerosis – a clinical study using myocardial T1-mapping and extracellular volume quantification , 2014, Journal of Cardiovascular Magnetic Resonance.
[35] 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.
[36] Xiang Li,et al. Estimating the ground truth from multiple individual segmentations incorporating prior pattern analysis with application to skin lesion segmentation , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[37] J. Pousin,et al. Evaluation of the Dynamic Deformable Elastic Template model for the segmentation of the heart in MRI sequences , 2009, The MIDAS Journal.
[38] Demetri Terzopoulos,et al. Snakes: Active contour models , 2004, International Journal of Computer Vision.
[39] M. Robson,et al. Cardiovascular magnetic resonance by non contrast T1-mapping allows assessment of severity of injury in acute myocardial infarction , 2012, Journal of Cardiovascular Magnetic Resonance.
[40] Sébastien Ourselin,et al. STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation , 2013, Medical Image Anal..
[41] Ben Glocker,et al. Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study , 2019, ArXiv.
[42] Mert R. Sabuncu,et al. Multi-atlas segmentation of biomedical images: A survey , 2014, Medical Image Anal..
[43] Caroline Petitjean,et al. A review of segmentation methods in short axis cardiac MR images , 2011, Medical Image Anal..
[44] Charles Blundell,et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.
[45] Matthew D. Robson,et al. Systolic ShMOLLI myocardial T1-mapping for improved robustness to partial-volume effects and applications in tachyarrhythmias , 2015, Journal of Cardiovascular Magnetic Resonance.
[46] P. Matthews,et al. Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches , 2013, Journal of Cardiovascular Magnetic Resonance.
[47] Konstantinos Kamnitsas,et al. Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth , 2017, IEEE Transactions on Medical Imaging.
[48] M. Robson,et al. Myocardial T1 mapping and extracellular volume quantification: a Society for Cardiovascular Magnetic Resonance (SCMR) and CMR Working Group of the European Society of Cardiology consensus statement , 2013, Journal of Cardiovascular Magnetic Resonance.
[49] Stefan Neubauer,et al. Myocardial Tissue Characterization Using Magnetic Resonance Noncontrast T1 Mapping in Hypertrophic and Dilated Cardiomyopathy , 2012, Circulation. Cardiovascular imaging.
[50] M. Robson,et al. Non-contrast T1-mapping detects acute myocardial edema with high diagnostic accuracy: a comparison to T2-weighted cardiovascular magnetic resonance , 2012, Journal of Cardiovascular Magnetic Resonance.
[51] S. K. White,et al. Normal variation of magnetic resonance T1 relaxation times in the human population at 1.5 T using ShMOLLI , 2013, Journal of Cardiovascular Magnetic Resonance.
[52] Nassir Navab,et al. Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling , 2018, MICCAI.
[53] Ben Glocker,et al. Automatic Quality Control of Cardiac MRI Segmentation in Large-Scale Population Imaging , 2017, MICCAI.
[54] Teng-Yi Huang,et al. Automatic regional analysis of myocardial native T1 values: left ventricle segmentation and AHA parcellations , 2017, The International Journal of Cardiovascular Imaging.
[55] Balaji Lakshminarayanan,et al. Deep Ensembles: A Loss Landscape Perspective , 2019, ArXiv.
[56] Stefan Neubauer,et al. T(1) mapping for the diagnosis of acute myocarditis using CMR: comparison to T2-weighted and late gadolinium enhanced imaging. , 2013, JACC. Cardiovascular imaging.