Multiple Sclerosis Lesion Synthesis in MRI Using an Encoder-Decoder U-NET
暂无分享,去创建一个
Joaquim Salvi | Xavier Lladó | Arnau Oliver | Mariano Cabezas | Sergi Valverde | Deborah Pareto | Mostafa Salem | Àlex Rovira | A. Oliver | À. Rovira | X. Lladó | J. Salvi | D. Pareto | M. Cabezas | S. Valverde | Mostafa Salem
[1] Sébastien Ourselin,et al. Bayesian Model Selection for Pathological Neuroimaging Data Applied to White Matter Lesion Segmentation , 2015, IEEE Transactions on Medical Imaging.
[2] O. Ciccarelli,et al. MRI CRITERIA FOR THE DIAGNOSIS OF MULTIPLE SCLEROSIS: MAGNIMS CONSENSUS GUIDELINES , 2016, The Lancet Neurology.
[3] P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .
[4] Stephen M. Smith,et al. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.
[5] Max A. Viergever,et al. Automatic Segmentation of MR Brain Images With a Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.
[6] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[7] Saurabh Jain,et al. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images , 2015, NeuroImage: Clinical.
[8] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[9] Simon K. Warfield,et al. Asymmetric Loss Functions and Deep Densely-Connected Networks for Highly-Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection , 2018, IEEE Access.
[10] Sotirios A. Tsaftaris,et al. Multimodal MR Synthesis via Modality-Invariant Latent Representation , 2018, IEEE Transactions on Medical Imaging.
[11] Shuiwang Ji,et al. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation , 2015, NeuroImage.
[12] Christopher Joseph Pal,et al. The Importance of Skip Connections in Biomedical Image Segmentation , 2016, LABELS/DLMIA@MICCAI.
[13] Joaquim Salvi,et al. A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis , 2017, NeuroImage: Clinical.
[14] Daniel Rueckert,et al. Pseudo-healthy Image Synthesis for White Matter Lesion Segmentation , 2016, SASHIMI@MICCAI.
[15] Alex Rovira,et al. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach , 2017, NeuroImage.
[16] John Muschelli,et al. MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions , 2018, Journal of neuroimaging : official journal of the American Society of Neuroimaging.
[17] Simon Andermatt,et al. Automated Segmentation of Multiple Sclerosis Lesions Using Multi-dimensional Gated Recurrent Units , 2017, BrainLes@MICCAI.
[18] Paul M. Thompson,et al. Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods , 2011, IEEE Transactions on Medical Imaging.
[19] F. Barkhof,et al. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis—clinical implementation in the diagnostic process , 2015, Nature Reviews Neurology.
[20] Alex Rovira,et al. Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches , 2012, Inf. Sci..
[21] Peter A. Calabresi,et al. A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions , 2010, NeuroImage.
[22] Brian B. Avants,et al. N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.
[23] Snehashis Roy,et al. Longitudinal multiple sclerosis lesion segmentation: Resource and challenge , 2017, NeuroImage.
[24] Marleen de Bruijne,et al. Why Does Synthesized Data Improve Multi-sequence Classification? , 2015, MICCAI.
[25] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[26] Jung-Hwan Oh,et al. Real Data Augmentation for Medical Image Classification , 2017, CVII-STENT/LABELS@MICCAI.
[27] Sébastien Ourselin,et al. Global image registration using a symmetric block-matching approach , 2014, Journal of medical imaging.
[28] B. Ginneken,et al. 3D Segmentation in the Clinic: A Grand Challenge , 2007 .
[29] M. Battaglini,et al. Evaluating and reducing the impact of white matter lesions on brain volume measurements , 2012, Human brain mapping.
[30] Sébastien Ourselin,et al. Fast free-form deformation using graphics processing units , 2010, Comput. Methods Programs Biomed..
[31] Christopher Joseph Pal,et al. Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..
[32] D. Rueckert,et al. White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks , 2017, NeuroImage: Clinical.
[33] Ben Glocker,et al. Is Synthesizing MRI Contrast Useful for Inter-modality Analysis? , 2013, MICCAI.
[34] Alex Rovira,et al. Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding , 2014, Comput. Methods Programs Biomed..
[35] Konstantinos Kamnitsas,et al. Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..
[36] Deniz Erdogmus,et al. Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks , 2017, MLMI@MICCAI.
[37] Christian Barillot,et al. Classification of multiple sclerosis lesions using adaptive dictionary learning , 2015, Comput. Medical Imaging Graph..
[38] Hayit Greenspan,et al. Multi-view longitudinal CNN for multiple sclerosis lesion segmentation , 2017, Eng. Appl. Artif. Intell..