MSSEG Challenge Proceedings: Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure
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[1] S Hannoun,et al. OFSEP, a nationwide cohort of people with multiple sclerosis: Consensus minimal MRI protocol. , 2015, Journal of neuroradiology. Journal de neuroradiologie.
[2] Olivier Commowick,et al. Longitudinal Intensity Normalization in Multiple Sclerosis Patients , 2014, CLIP@MICCAI.
[3] Simon K. Warfield,et al. A Logarithmic Opinion Pool Based STAPLE Algorithm for the Fusion of Segmentations With Associated Reliability Weights , 2014, IEEE Transactions on Medical Imaging.
[4] A. Oliver,et al. A toolbox for multiple sclerosis lesion segmentation , 2015, Neuroradiology.
[5] Ying Wu,et al. Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI , 2006, NeuroImage.
[6] Luca Maria Gambardella,et al. Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.
[7] Olivier Clatz,et al. Detection of DTI White Matter Abnormalities in Multiple Sclerosis Patients , 2008, MICCAI.
[8] Alan C. Evans,et al. A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.
[9] Abel G. Silva-Filho,et al. Evaluation-Oriented Training via Surrogate Metrics for Multiple Sclerosis Segmentation , 2016, MICCAI.
[10] Wilfried Philips,et al. MRI Segmentation of the Human Brain: Challenges, Methods, and Applications , 2015, Comput. Math. Methods Medicine.
[11] D. Collins,et al. Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.
[12] F. Barkhof,et al. Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs)☆ , 2013, NeuroImage: Clinical.
[13] Daniel Rueckert,et al. Multiple Sclerosis Lesion Segmentation Using Dictionary Learning and Sparse Coding , 2013, MICCAI.
[14] David A. Rottenberg,et al. Quantitative comparison of four brain extraction algorithms , 2004, NeuroImage.
[15] Philippe Salembier,et al. Antiextensive connected operators for image and sequence processing , 1998, IEEE Trans. Image Process..
[16] Joachim Weickert. E � cient and Reliable Schemes for Nonlinear Di usion Filtering , 2005 .
[17] Brian B. Avants,et al. N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.
[18] T. Arbel,et al. HIERARCHICAL MRF AND RANDOM FOREST SEGMENTATION OF MS LESIONS AND HEALTHY TISSUES IN BRAIN MRI , 2015 .
[19] Bernhard Hemmer,et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis , 2012, NeuroImage.
[20] Lisa Tang,et al. Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation , 2016, IEEE Transactions on Medical Imaging.
[21] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[22] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] G. Barker,et al. Quantification of MRI lesion load in multiple sclerosis: a comparison of three computer-assisted techniques. , 1996, Magnetic resonance imaging.
[24] Christian Barillot,et al. A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation , 2010, AISTATS.
[25] Alex Rovira,et al. Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding , 2014, Comput. Methods Programs Biomed..
[26] Olivier Commowick,et al. Block-matching strategies for rigid registration of multimodal medical images , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).
[27] April Khademi,et al. Robust White Matter Lesion Segmentation in FLAIR MRI , 2012, IEEE Transactions on Biomedical Engineering.
[28] Koenraad Van Leemput,et al. N3 Bias Field Correction Explained as a Bayesian Modeling Method , 2014, BAMBI.
[29] D. Louis Collins,et al. Trimmed-Likelihood Estimation for Focal Lesions and Tissue Segmentation in Multisequence MRI for Multiple Sclerosis , 2011, IEEE Transactions on Medical Imaging.
[30] Max A. Viergever,et al. Automatic Segmentation of MR Brain Images With a Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.
[31] Koenraad Van Leemput,et al. Automated model-based tissue classification of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.
[32] Olivier Commowick,et al. Robust detection of multiple sclerosis lesions from intensity-normalized multi-channel MRI , 2015, Medical Imaging.
[33] Pierrick Coupé,et al. volBrain: An Online MRI Brain Volumetry System , 2015, Front. Neuroinform..
[34] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[35] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[36] Xiaogang Wang,et al. Fully Convolutional Neural Networks for Crowd Segmentation , 2014, ArXiv.
[37] Jeffrey A. Cohen,et al. Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria , 2011, Annals of neurology.
[38] A. Evans,et al. MRI simulation-based evaluation of image-processing and classification methods , 1999, IEEE Transactions on Medical Imaging.
[39] Shuiwang Ji,et al. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation , 2015, NeuroImage.
[40] Annegreet van Opbroek,et al. Automated Brain-Tissue Segmentation by Multi-Feature SVM Classification , 2013 .
[41] Leon A. Gatys,et al. A Neural Algorithm of Artistic Style , 2015, ArXiv.
[42] Allan Hanbury,et al. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool , 2015, BMC Medical Imaging.
[43] Sébastien Ourselin,et al. Block Matching: A General Framework to Improve Robustness of Rigid Registration of Medical Images , 2000, MICCAI.
[44] Christian Barillot,et al. Adaptive weighted fusion of multiple MR sequences for brain lesion segmentation , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[45] H. Sebastian Seung,et al. Natural Image Denoising with Convolutional Networks , 2008, NIPS.
[46] Jitendra Malik,et al. Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..
[47] E. Yeterian,et al. MRI-Based Topographic Parcellation of Human Cerebral White Matter and Nuclei II. Rationale and Applications with Systematics of Cerebral Connectivity , 1999, NeuroImage.
[48] D. Louis Collins,et al. Multiple Sclerosis lesion segmentation using an automated multimodal Graph Cut , 2016 .
[49] Hayit Greenspan,et al. LESION DETECTION IN NOISY MR BRAIN IMAGES USING CONSTRAINED GMM AND ACTIVE CONTOURS , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[50] P. Narayana,et al. A comprehensive approach to the segmentation of multichannel three-dimensional MR brain images in multiple sclerosis☆ , 2013, NeuroImage: Clinical.
[51] H. Handels. MS-LESION SEGMENTATION IN MRI WITH RANDOM FORESTS , 2015 .
[52] Yann LeCun,et al. What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[53] Simon K. Warfield,et al. A Model of Population and Subject (MOPS) Intensities With Application to Multiple Sclerosis Lesion Segmentation , 2015, IEEE Transactions on Medical Imaging.
[54] Simon K. Warfield,et al. Automatic segmentation of newborn brain MRI , 2009, NeuroImage.
[55] Olivier Clatz,et al. Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images , 2011, NeuroImage.
[56] D. Louis Collins,et al. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging , 2013, Medical Image Anal..
[57] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[58] Yongchao Xu,et al. Context-based energy estimator: Application to object segmentation on the tree of shapes , 2012, 2012 19th IEEE International Conference on Image Processing.
[59] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[60] Thomas Samaille,et al. Contrast-Based Fully Automatic Segmentation of White Matter Hyperintensities: Method and Validation , 2012, PloS one.
[61] Gilles Louppe,et al. Independent consultant , 2013 .
[62] Alex Rovira,et al. Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches , 2012, Inf. Sci..
[63] Simon K. Warfield,et al. Automated delineation of white matter fiber tracts with a multiple region-of-interest approach , 2012, NeuroImage.
[64] G. Comi,et al. Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis. , 1997, Brain : a journal of neurology.
[65] W. Eric L. Grimson,et al. Adaptive Segmentation of MRI Data , 1995, CVRMed.
[66] B. Ginneken,et al. 3D Segmentation in the Clinic: A Grand Challenge , 2007 .
[67] Alex Rovira,et al. MARGA: Multispectral Adaptive Region Growing Algorithm for brain extraction on axial MRI , 2014, Comput. Methods Programs Biomed..
[68] Gilles Celeux,et al. EM procedures using mean field-like approximations for Markov model-based image segmentation , 2003, Pattern Recognit..
[69] Koenraad Van Leemput,et al. Automated segmentation of multiple sclerosis lesions by model outlier detection , 2001, IEEE Transactions on Medical Imaging.
[70] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[71] S. Carlsson,et al. Segmentation of White Matter Lesions Using Multispectral MRI and Cascade of Support Vector Machines with Active Learning , 2011 .
[72] Roberto Cipolla,et al. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[73] Chao Li,et al. Automatic segmentation and volumetric quantification of white matter hyperintensities on fluid-attenuated inversion recovery images using the extreme value distribution , 2015, Neuroradiology.
[74] Shinto Eguchi,et al. Spontaneous Clustering via Minimum Gamma-Divergence , 2014, Neural Computation.
[75] Michael J Ackerman,et al. Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit. , 2002, Studies in health technology and informatics.
[76] N. Otsu. A threshold selection method from gray level histograms , 1979 .
[77] Graham W. Taylor,et al. Adaptive deconvolutional networks for mid and high level feature learning , 2011, 2011 International Conference on Computer Vision.
[78] Pierrick Coupé,et al. An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images , 2008, IEEE Transactions on Medical Imaging.
[79] Karl J. Friston,et al. Unified segmentation , 2005, NeuroImage.
[80] Grégoire Malandain,et al. An Automatic Segmentation of T2-FLAIR Multiple Sclerosis Lesions , 2008, The MIDAS Journal.
[81] K. Misulis,et al. DEMYELINATING diseases. , 1952, Lancet.
[82] P. Santago,et al. Quantification of MR brain images by mixture density and partial volume modeling , 1993, IEEE Trans. Medical Imaging.
[83] April Khademi,et al. Generalized method for partial volume estimation and tissue segmentation in cerebral magnetic resonance images , 2014, Journal of medical imaging.
[84] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[85] Amir Alansary,et al. MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans , 2015, Comput. Intell. Neurosci..