Automatic spatial estimation of white matter hyperintensities evolution in brain MRI using disease evolution predictor deep neural networks
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Taku Komura | Muhammad Febrian Rachmadi | Joanna Wardlaw | Stephen Makin | Maria del C. Valdés-Hernández | T. Komura | J. Wardlaw | M. F. Rachmadi | S. Makin | M. F. Rachmadi | Maria del C. Valdés-Hernández
[1] D. Altman,et al. STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.
[2] Silvio Savarese,et al. Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[3] Bjoern H. Menze,et al. Neural parameters estimation for brain tumor growth modeling , 2019, MICCAI.
[4] P. Scheltens,et al. White matter hyperintensities, cognitive impairment and dementia: an update , 2015, Nature Reviews Neurology.
[5] Dong Won Yang,et al. White Matter Hyperintensity in Ischemic Stroke Patients: It May Regress Over Time , 2015, Journal of stroke.
[6] L. R. Dice. Measures of the Amount of Ecologic Association Between Species , 1945 .
[7] Frederik Barkhof,et al. Progression of White Matter Hyperintensities and Incidence of New Lacunes Over a 3-Year Period: The Leukoaraiosis and Disability Study , 2008, Stroke.
[8] S Ropele,et al. Risk factors and progression of small vessel disease-related cerebral abnormalities. , 2002, Journal of neural transmission. Supplementum.
[9] Taku Komura,et al. Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map , 2019, bioRxiv.
[10] Paul Babyn,et al. Generative Adversarial Network in Medical Imaging: A Review , 2018, Medical Image Anal..
[11] S. Allassonnière,et al. Using longitudinal metamorphosis to examine ischemic stroke lesion dynamics on perfusion-weighted images and in relation to final outcome on T2-w images , 2014, NeuroImage: Clinical.
[12] Wei-Shi Zheng,et al. Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images , 2018, NeuroImage.
[13] Joanna M. Wardlaw,et al. On the computational assessment of white matter hyperintensity progression: difficulties in method selection and bias field correction performance on images with significant white matter pathology , 2016, Neuroradiology.
[14] P. Scheltens,et al. Measuring progression of cerebral white matter lesions on MRI , 2004, Neurology.
[15] Kyong Hwan Jin,et al. Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging , 2017, Behavioural Brain Research.
[16] F. X. Aymerich,et al. Decreased white matter lesion volume and improved cognitive function after liver transplantation , 2007, Hepatology.
[17] Henri A. Vrooman,et al. Progression of Cerebral Small Vessel Disease in Relation to Risk Factors and Cognitive Consequences: Rotterdam Scan Study , 2008, Stroke.
[18] Nick C Fox,et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration , 2013, The Lancet Neurology.
[19] Joanna M. Wardlaw,et al. White matter hyperintensity reduction and outcomes after minor stroke , 2017, Neurology.
[20] Xiao Luo,et al. Microstructural and metabolic changes in the longitudinal progression of white matter hyperintensities , 2019, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.
[21] D. Rueckert,et al. White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks , 2017, NeuroImage: Clinical.
[22] Tolga Tasdizen,et al. Adversarial regression training for visualizing the progression of chronic obstructive pulmonary disease with chest x-rays , 2019, MICCAI.
[23] W. J. Langford. Statistical Methods , 1959, Nature.
[24] P. Matthews,et al. White matter lesion progression, brain atrophy, and cognitive decline: The Austrian stroke prevention study , 2005, Annals of neurology.
[25] W. M. van der Flier,et al. Reliability and Sensitivity of Visual Scales versus Volumetry for Evaluating White Matter Hyperintensity Progression , 2008, Cerebrovascular Diseases.
[26] Taku Komura,et al. Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology , 2018, Comput. Medical Imaging Graph..
[27] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[28] Fuqiang Gao,et al. Dynamic Progression of White Matter Hyperintensities in Alzheimer’s Disease and Normal Aging: Results from the Sunnybrook Dementia Study , 2016, Front. Aging Neurosci..
[29] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[30] Qingmao Hu,et al. Macroscopic Cerebral Tumor Growth Modeling From Medical Images: A Review , 2018, IEEE Access.
[31] J. Wardlaw,et al. Rationale, design and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke , 2015, Brain and behavior.
[32] Ender Konukoglu,et al. Visual Feature Attribution Using Wasserstein GANs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[33] Andrea C. Bozoki,et al. Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification , 2016, PloS one.
[34] I. V. van Uden,et al. White Matter Microstructural Damage on Diffusion Tensor Imaging in Cerebral Small Vessel Disease: Clinical Consequences , 2016, Stroke.
[35] Martin Wolf,et al. Exergame and Balance Training Modulate Prefrontal Brain Activity during Walking and Enhance Executive Function in Older Adults , 2016, Front. Aging Neurosci..
[36] Taku Komura,et al. Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression using Irregularity Age Map in Brain MRI , 2018, bioRxiv.
[37] Reinhold Schmidt,et al. Longitudinal change of small-vessel disease-related brain abnormalities , 2016, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.
[38] Michael Brady,et al. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.
[39] T. Nishimura,et al. Cerebral White Matter Lesions May Be Partially Reversible in Patients with Carotid Artery Stenosis , 2010, American Journal of Neuroradiology.
[40] R D Rudolf,et al. High Blood Pressure , 1937, Canadian Medical Association journal.
[41] M. Fornage,et al. Genetic variants of the NOTCH3 gene in the elderly and magnetic resonance imaging correlates of age-related cerebral small vessel disease , 2011, Brain : a journal of neurology.
[42] F. de Leeuw,et al. Disease progression and regression in sporadic small vessel disease-insights from neuroimaging. , 2017, Clinical science.
[43] P. Scheltens,et al. Progression of cerebral white matter hyperintensities on MRI is related to diastolic blood pressure , 1998, Neurology.
[44] I. Deary,et al. Towards the automatic computational assessment of enlarged perivascular spaces on brain magnetic resonance images: A systematic review , 2013, Journal of magnetic resonance imaging : JMRI.
[45] D. Norris,et al. Cognitive consequences of regression of cerebral small vessel disease , 2019, European stroke journal.
[46] Hao Su,et al. A Point Set Generation Network for 3D Object Reconstruction from a Single Image , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] D. Norris,et al. Nonlinear temporal dynamics of cerebral small vessel disease , 2017, Neurology.
[48] Attenuation of Brain White Matter Hyperintensities after Cerebral Infarction , 2009, American Journal of Neuroradiology.
[49] Taku Komura,et al. Deep Learning vs. Conventional Machine Learning: Pilot Study of WMH Segmentation in Brain MRI with Absence or Mild Vascular Pathology , 2017, Journal of Imaging.
[50] Christian Enzinger,et al. Progression of cerebral white matter lesions: 6-year results of the Austrian Stroke Prevention Study , 2003, The Lancet.
[51] William M. Wells,et al. Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.
[52] C. Jack,et al. Smoking and white matter hyperintensity progression , 2015, Neurology.
[53] Andrei V. Alexandrov,et al. Brain and Behavior , 2014, Brain and Behavior.
[54] Sotirios A. Tsaftaris,et al. Medical Image Computing and Computer Assisted Intervention , 2017 .
[55] Daniel Rueckert,et al. Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images , 2019, Comput. Medical Imaging Graph..
[56] Nassir Navab,et al. GANs for Medical Image Analysis , 2018, Artif. Intell. Medicine.
[57] Pietro Liò,et al. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease , 2018, NeuroImage.
[58] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[59] À. Rovira,et al. Decrease in the Volume of White Matter Lesions with Improvement of Hepatic Encephalopathy , 2007, American Journal of Neuroradiology.
[60] Taku Komura,et al. Automatic Irregular Texture Detection in Brain MRI Without Human Supervision , 2018, MICCAI.
[61] B. Mazoyer,et al. Longitudinal follow-up of individual white matter hyperintensities in a large cohort of elderly , 2009, Neuroradiology.
[62] J. Bugnicourt,et al. Attenuation of Brain White Matter Lesions After Lacunar Stroke , 2012, International journal of preventive medicine.
[63] Taku Komura,et al. Dilated Saliency U-Net for White Matter Hyperintensities Segmentation Using Irregularity Age Map , 2019, bioRxiv.
[64] Simon Andermatt,et al. Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge , 2019, IEEE Transactions on Medical Imaging.
[65] Hervé Delingette,et al. Tumor growth parameters estimation and source localization from a unique time point: Application to low-grade gliomas , 2013, Comput. Vis. Image Underst..
[66] OphéliaGodin,et al. Antihypertensive Treatment and Change in Blood Pressure Are Associated With the Progression of White Matter Lesion Volumes , 2011 .
[67] B. Mazoyer,et al. Apolipoprotein E Genotype Is Related to Progression of White Matter Lesion Load , 2009, Stroke.
[68] Vikas Singh,et al. Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population , 2011, NeuroImage.
[69] Thomas Brox,et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.
[70] F. Fazekas,et al. Evolution of White Matter Lesions , 2002, Cerebrovascular Diseases.
[71] Xiaojun Xu,et al. Associations between APOE genotype and cerebral small-vessel disease: a longitudinal study , 2017, Oncotarget.
[72] Wiro J Niessen,et al. High Blood Pressure and Cerebral White Matter Lesion Progression in the General Population , 2013, Hypertension.
[73] J. Wardlaw,et al. Sample size considerations for trials using cerebral white matter hyperintensity progression as an intermediate outcome at 1 year after mild stroke: results of a prospective cohort study , 2017, Trials.
[74] Klaus H. Maier-Hein,et al. Deep Probabilistic Modeling of Glioma Growth , 2019, MICCAI.
[75] Owen Carmichael,et al. White Matter Hyperintensity Penumbra , 2011, Stroke.
[76] Owen Carmichael,et al. White Matter Hyperintensities and Their Penumbra Lie Along a Continuum of Injury in the Aging Brain , 2014, Stroke.
[77] S. Allassonnière,et al. Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal , 2012, NeuroImage: Clinical.
[78] Aaron C. Courville,et al. FiLM: Visual Reasoning with a General Conditioning Layer , 2017, AAAI.