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A method for fully automated measurement of neurological structures in MRI

Edward A. Ashton, Jonathan K. Riek, Larry Molinelli, Michel J. Berg, Kevin J. Parker, Milan Sonka, J. Michael Fitzpatrick
2003 Medical Imaging 2003: Image Processing  
The detection and measurement of abnormal structures, such as white matter lesions in multiple sclerosis patients, requires a slightly different approach.  ...  Lesions are detected through the application of a spectral matched filter to areas identified by the classifier as white matter.  ...  variability study for white matter lesion measurement.  ... 
doi:10.1117/12.481390 dblp:conf/miip/AshtonRMBP03 fatcat:piiodkuu3fhyfj5utukk44fen4

Regional White Matter Hyperintensity Burden in Automated Segmentation Distinguishes Late-Life Depressed Subjects From Comparison Subjects Matched for Vascular Risk Factors

Yvette I. Sheline, Joseph L. Price, S. Neil Vaishnavi, Mark A. Mintun, Deanna M. Barch, Adrian A. Epstein, Consuelo H. Wilkins, Abraham Z. Snyder, Lars Couture, Kenneth Schechtman, Robert C. McKinstry
2008 American Journal of Psychiatry  
Correlations between neuropsychological performance and whole brain-segmented white matter hyperintensities and white and gray matter volumes were also examined.  ...  Automated segmentation methods were used to compare the total brain and regional white matter hyperintensity burden between depressed patients and comparison subjects.  ...  A preliminary version of this study was presented as an abstract at the 2005 annual meeting of the American College of Neuropsychopharmacology.  ... 
doi:10.1176/appi.ajp.2007.07010175 pmid:18281408 pmcid:PMC4118770 fatcat:c2scbipsjnegroqydp4eldvjxu

A Semi-Automated Method for Measuring White Matter Hyperintensity Volume

YongSoo Shim, Bora Yoon, Yun Jeong Hong, A-Hyun Cho, Dong-Won Yang
2013 Dementia and Neurocognitive Disorders  
The semi-automated volume measuring method of WMHs, with ANALYZE, was a valid and reliable method to quantify subcortical white matter damages from various etiologies.  ...  Conclusions: The semi-automated volume measurement of the WMHs with Analyze was a valid and a reliable method to quantify subcortical white matter damages of various etiologies. may impair the diagnostic  ...  Lesions were marked and the borders were set, using a local threshold on each slice. All deep white matter lesions as well as periventricular lesions were included.  ... 
doi:10.12779/dnd.2013.12.1.21 fatcat:q6pwhtr5gnecviydwpvd73f5za

Volumetric Analysis from a Harmonized Multisite Brain MRI Study of a Single Subject with Multiple Sclerosis

R.T. Shinohara, J. Oh, G. Nair, P.A. Calabresi, C. Davatzikos, J. Doshi, R.G. Henry, G. Kim, K.A. Linn, N. Papinutto, D. Pelletier, D.L. Pham (+9 others)
2017 American Journal of Neuroradiology  
Site also explained Ͼ80% of the variation in most automated volumetric measurements.  ...  Several automated lesion-detection and whole-brain, cortical, and deep gray matter volumetric pipelines were applied.  ...  ACKNOWLEDGMENTS The following is a full list of individuals who contributed to this NAIMS study-Brigham and Women's Hospital, Harvard Medical School (Boston, Massachusetts): Rohit Bakshi, Renxin Chu, Gloria  ... 
doi:10.3174/ajnr.a5254 pmid:28642263 pmcid:PMC5557658 fatcat:4m27i4ciefa4dc6jksdcoam66u

Fractional Segmentation of White Matter [chapter]

Simon K. Warfield, Carl-Fredrik Westin, Charles R. G. Guttmann, Marilyn Albert, Ferenc A. Jolesz, Ron Kikinis
1999 Lecture Notes in Computer Science  
present at each voxel of the white matter.  ...  We investigated the ability to characterize these different subject groups based upon tissue volumes determined by spatially varying classification, and by the fractional segmentation of the white matter  ...  of abnormal white matter volume to intrancranial cavity volume, as a function of age, for normal aging (NA, o) and Alzheimer's disease (AD, plot of white matter, lesion and CSF tissue volume to ICC volume  ... 
doi:10.1007/10704282_7 fatcat:xqvv2wnoinhkjgfoilylrtkknm

Detection of Epileptogenic Cortical Malformations with Surface-Based MRI Morphometry

Thomas Thesen, Brian T. Quinn, Chad Carlson, Orrin Devinsky, Jonathan DuBois, Carrie R. McDonald, Jacqueline French, Richard Leventer, Olga Felsovalyi, Xiuyuan Wang, Eric Halgren, Ruben Kuzniecky (+1 others)
2011 PLoS ONE  
normal control group to detect clusters of abnormal cortical thickness, gray-white matter contrast, local gyrification, sulcal depth, jacobian distance and curvature.  ...  Here we use an automated, surfacebased method for quantifying morphometric features related to epileptogenic cortical malformations to detect abnormal cortical thickness and blurred gray-white matter boundaries  ...  First, an estimate of the gray/white matter boundary was constructed by classifying all white matter voxels in the MRI volume.  ... 
doi:10.1371/journal.pone.0016430 pmid:21326599 pmcid:PMC3033882 fatcat:26hofbeokjgxfbgtt4aqydrlve

Segmentation of brain magnetic resonance images for measurement of gray matter atrophy in multiple sclerosis patients

Kunio Nakamura, Elizabeth Fisher
2009 NeuroImage  
Multiple sclerosis (MS) affects both white matter and gray matter (GM).  ...  Tests of the effects of varying the size of MS lesions revealed a moderate and consistent dependence of GM volumes on T2 lesion volume, which suggests that GM volumes should be corrected for T2 lesion  ...  The authors would like to thank Patricia Jagodnik for the help with image data management and Smitha Thomas for evaluation of gray matter segmentation results.  ... 
doi:10.1016/j.neuroimage.2008.09.059 pmid:19007895 pmcid:PMC3001325 fatcat:6gcxdoashbcclkbps6upsi2bii

Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI

Ying Wu, Simon K. Warfield, I. Leng Tan, William M. Wells, Dominik S. Meier, Ronald A. van Schijndel, Frederik Barkhof, Charles R.G. Guttmann
2006 NeuroImage  
Conclusion: 3ch-TDS + is a promising method for automated segmentation of MS lesion subtypes. D  ...  Intensity-based statistical k-nearest neighbor (k-NN) classification was combined with template-driven segmentation and partial volume artifact correction (TDS + ) for segmentation of MS lesions subtypes  ...  National Multiple Sclerosis Society (RG 3574-A-1); a grant form the Dutch Foundation for MS Research (Voorschoten, The Netherlands); grants from the National Institutes of Health (P41 RR13218-01; R01 NS35142  ... 
doi:10.1016/j.neuroimage.2006.04.211 pmid:16797188 fatcat:hbbtiadvwjawreq2aroojfjbxm

MIMoSA: A Method for Inter-Modal Segmentation Analysis [article]

Alessandra Valcarcel, Kristin Linn, Simon Vandekar, Theodore Satterthwaite, Peter Calabresi, Dzung Pham, Russell Shinohara
2017 bioRxiv   pre-print
Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis.  ...  any voxel is part of a lesion.  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.  ... 
doi:10.1101/150284 fatcat:5frtxzrkgvb3npptsdtl6sybee

Whole-brain atrophy in multiple sclerosis measured by automated versus semiautomated MR imaging segmentation

Jitendra Sharma, Michael P Sanfilipo, Ralph H B Benedict, Bianca Weinstock-Guttman, Frederick E Munschauer, Rohit Bakshi
2004 American Journal of Neuroradiology  
Linear measures of atrophy, whole-brain lesion volumes, and clinical data were used to explore validity. The 2D automated method yielded unreliable segmentation and was discarded.  ...  These automated and semiautomated measures of whole-brain atrophy provided similar and nearly interchangeable data regarding MS.  ...  The automated method we tested also has the advantage of separating the brain parenchyma into gray matter and white matter compartments, allowing a compartmental analysis of gray versus white matter atrophy  ... 
pmid:15205136 pmcid:PMC7975687 fatcat:n6uzmh2mbzdyld5ohhpsb3uvfi

Measurement of atrophy in multiple sclerosis: pathological basis, methodological aspects and clinical relevance

D. H. Miller
2002 Brain  
Abbreviations: BFV = brain fraction volume; BICCR = brain intracranial capacity ratio; BPF = brain parenchymal fraction; CIS = clinically isolated syndromes; COV = coef®cient of variance; EDSS = expanded  ...  matter; SPM = statistical parametric mapping; WBR = whole brain ratio ã  ...  Many automated methods exist for segmentation (and thus volume measurement) of the brain parenchyma, and for brain white and grey matter.  ... 
doi:10.1093/brain/awf177 pmid:12135961 fatcat:zxyfwfxaizhira3tzf25x4ef6y

MSJ789884_supplementary_appendix – Supplemental material for Efficacy and safety of ozanimod in multiple sclerosis: Dose-blinded extension of a randomized phase II study

Jeffrey A Cohen, Giancarlo Comi, Douglas L Arnold, Amit Bar-Or, Krzysztof W Selmaj, Lawrence Steinman, Eva K Havrdová, Bruce AC Cree, Xavier Montalbán, Hans-Peter Hartung, Vivian Huang, Paul Frohna (+2 others)
2018 Figshare  
Supplemental material, MSJ789884_supplementary_appendix for Efficacy and safety of ozanimod in multiple sclerosis: Dose-blinded extension of a randomized phase II study by Jeffrey A Cohen, Giancarlo Comi  ...  , white matter, spinal cord) (e.g. absolute tissue volume in ml, tissue volume as a fraction of intracranial volume, percentage change in tissue volumes) Analysis software Manual method Output measure  ...  MTR, DTI, T1-RT, T2-RT, T2*, T2', 1 H-MRS, perfusion, Na) Type (e.g. whole brain, grey matter, white matter, spinal cord, normal-appearing grey matter or white matter) Type (e.g. whole brain, grey matter  ... 
doi:10.25384/sage.6865814.v1 fatcat:fouybnbeybcqhe2xuzgqenyv6u


Ivan Dimitrov, Radoslav Georgiev, Ara Kaprelyan, Yavor Enchev, Margarita Grudkova, Nataliya Usheva, Borislav Ivanov
2016 Journal of IMAB  
Results: There were statistically significant differences for white matter volume before and after lesion filling (p<0.05). No other volumes were significantly different.  ...  Volumes of brain grey and white matter, peripheral grey matter and ventricle CSF were calculated using SIENAX for the filled and non-filled series, which were then compared.  ...  The purpose of the present study is to assess whether the filling of white matter lesions on T1 weighted 3-D MRI scans of MS patients would influence the values of total brain volume, grey and white matter  ... 
doi:10.5272/jimab.2016221.1029 fatcat:sgrkrwltincpxpls2nwaqqacgi

The Relationships among MRI-Defined Spinal Cord Involvement, Brain Involvement, and Disability in Multiple Sclerosis

Adam B. Cohen, Mohit Neema, Ashish Arora, Elisa Dell'Oglio, Ralph H. B. Benedict, Shahamat Tauhid, Daniel Goldberg-Zimring, Christian Chavarro-Nieto, Antonella Ceccarelli, Joshua P. Klein, James M. Stankiewicz, Maria K. Houtchens (+4 others)
2011 Journal of Neuroimaging  
range of other CNS measures of lesions and atrophy, including thoracic or whole spinal cord volume, and cerebral gray, white or whole brain volume.  ...  The brain-cord relationships between whole or regional spinal cord volume or lesions and gray matter, white matter, or whole brain volume or whole brain lesions were generally weak and all non-significant  ...  These findings were presented in preliminary form at the 62 nd annual meeting of the American Academy of Neurology, Toronto, Canada, April 10-17, 2010  ... 
doi:10.1111/j.1552-6569.2011.00589.x pmid:21447024 pmcid:PMC3128174 fatcat:pyi742e7qva3pks75oyb4ofms4

Automated Integration of Multimodal MRI for the Probabilistic Detection of the Central Vein Sign in White Matter Lesions

J.D. Dworkin, P. Sati, A. Solomon, D.L. Pham, R. Watts, M.L. Martin, D. Ontaneda, M.K. Schindler, D.S. Reich, R.T. Shinohara
2018 American Journal of Neuroradiology  
In this study, we present an automated technique for the detection of the central vein sign in white matter lesions.  ...  CONCLUSIONS: The current study presents the first fully automated method for detecting the central vein sign in white matter lesions and demonstrates promising performance in a sample of patients with  ...  performance of a fully automated method for detecting CVS in white matter lesions.  ... 
doi:10.3174/ajnr.a5765 pmid:30213803 pmcid:PMC6177309 fatcat:plqin7fdb5fmfhitrug673nm7e
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