Automatic identification of atherosclerosis subjects in a heterogeneous MR brain imaging data set
Carotid-artery atherosclerosis (CA) contributes significantly to overall morbidity and mortality in ischemic stroke. We propose a machine learning technique to automatically identify subjects with CA from a heterogeneous cohort of magnetic resonance brain images. The cohort includes 190 subjects wit...
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| Published in | Magnetic resonance imaging Vol. 62; pp. 18 - 27 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier Inc
01.10.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0730-725X 1873-5894 1873-5894 |
| DOI | 10.1016/j.mri.2019.06.007 |
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| Abstract | Carotid-artery atherosclerosis (CA) contributes significantly to overall morbidity and mortality in ischemic stroke. We propose a machine learning technique to automatically identify subjects with CA from a heterogeneous cohort of magnetic resonance brain images. The cohort includes 190 subjects with CA, white mater hyperintensites of presumed vascular origin or multiple sclerosis, as well as 211 presumed healthy subjects. We determined a set of handcrafted and convolutional discriminant features to perform this task. A support vector machine (SVM) was used to perform this four-class classification task. Our approach had an accuracy rate of 97.5% (higher than chance accuracy of 52.6% for guessing majority class), sensitivity of 96.4% and specificity of 97.9% in identifying subjects with CA, suggesting that the proposed combination of features may be used as an imaging biomarker for characterizing atherosclerotic disease on brain imaging. |
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| AbstractList | Carotid-artery atherosclerosis (CA) contributes significantly to overall morbidity and mortality in ischemic stroke. We propose a machine learning technique to automatically identify subjects with CA from a heterogeneous cohort of magnetic resonance brain images. The cohort includes 190 subjects with CA, white mater hyperintensites of presumed vascular origin or multiple sclerosis, as well as 211 presumed healthy subjects. We determined a set of handcrafted and convolutional discriminant features to perform this task. A support vector machine (SVM) was used to perform this four-class classification task. Our approach had an accuracy rate of 97.5% (higher than chance accuracy of 52.6% for guessing majority class), sensitivity of 96.4% and specificity of 97.9% in identifying subjects with CA, suggesting that the proposed combination of features may be used as an imaging biomarker for characterizing atherosclerotic disease on brain imaging. Carotid-artery atherosclerosis (CA) contributes significantly to overall morbidity and mortality in ischemic stroke. We propose a machine learning technique to automatically identify subjects with CA from a heterogeneous cohort of magnetic resonance brain images. The cohort includes 190 subjects with CA, white mater hyperintensites of presumed vascular origin or multiple sclerosis, as well as 211 presumed healthy subjects. We determined a set of handcrafted and convolutional discriminant features to perform this task. A support vector machine (SVM) was used to perform this four-class classification task. Our approach had an accuracy rate of 97.5% (higher than chance accuracy of 52.6% for guessing majority class), sensitivity of 96.4% and specificity of 97.9% in identifying subjects with CA, suggesting that the proposed combination of features may be used as an imaging biomarker for characterizing atherosclerotic disease on brain imaging.Carotid-artery atherosclerosis (CA) contributes significantly to overall morbidity and mortality in ischemic stroke. We propose a machine learning technique to automatically identify subjects with CA from a heterogeneous cohort of magnetic resonance brain images. The cohort includes 190 subjects with CA, white mater hyperintensites of presumed vascular origin or multiple sclerosis, as well as 211 presumed healthy subjects. We determined a set of handcrafted and convolutional discriminant features to perform this task. A support vector machine (SVM) was used to perform this four-class classification task. Our approach had an accuracy rate of 97.5% (higher than chance accuracy of 52.6% for guessing majority class), sensitivity of 96.4% and specificity of 97.9% in identifying subjects with CA, suggesting that the proposed combination of features may be used as an imaging biomarker for characterizing atherosclerotic disease on brain imaging. |
| Author | Souza, Roberto Rittner, Letícia Bento, Mariana Salluzzi, Marina Zhang, Yunyan Frayne, Richard |
| Author_xml | – sequence: 1 givenname: Mariana surname: Bento fullname: Bento, Mariana email: mariana.pinheirobent@ucalgary.ca organization: Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada – sequence: 2 givenname: Roberto surname: Souza fullname: Souza, Roberto organization: Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada – sequence: 3 givenname: Marina surname: Salluzzi fullname: Salluzzi, Marina organization: Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada – sequence: 4 givenname: Letícia surname: Rittner fullname: Rittner, Letícia organization: Medical Image Computing Laboratory (MICLab), School of Electrical and Computer Engineering, University of Campinas, Campinas, SP, Brazil – sequence: 5 givenname: Yunyan surname: Zhang fullname: Zhang, Yunyan organization: Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada – sequence: 6 givenname: Richard surname: Frayne fullname: Frayne, Richard organization: Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31228556$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_bspc_2022_103786 crossref_primary_10_3390_bioengineering9120783 crossref_primary_10_3389_fninf_2021_805669 crossref_primary_10_1109_ACCESS_2025_3549269 crossref_primary_10_3389_fneur_2023_1294723 crossref_primary_10_3390_s21248507 crossref_primary_10_1155_2022_2456550 crossref_primary_10_32725_jab_2022_008 crossref_primary_10_1016_j_jstrokecerebrovasdis_2020_105162 |
| Cites_doi | 10.1117/1.JMI.2.1.014002 10.1109/TMI.2019.2905770 10.1371/journal.pone.0165719 10.1016/j.mri.2014.01.017 10.1080/01621459.1955.10501294 10.1007/978-3-319-67810-8 10.1259/bjr/74316620 10.1016/j.media.2005.09.004 10.1016/j.bspc.2006.05.002 10.1146/annurev-bioeng-071516-044442 10.1016/j.mri.2003.09.001 10.1016/j.mri.2014.04.016 10.1016/j.neuroimage.2016.07.027 10.1214/aoms/1177731944 10.1007/s13244-018-0639-9 10.1016/j.media.2017.07.005 10.1016/j.neuroimage.2016.07.018 10.1016/j.nicl.2017.10.007 10.1109/TMI.2016.2528162 10.1016/j.neuroimage.2011.09.015 10.1016/S1474-4422(13)70124-8 10.1109/TMI.2010.2046908 10.1016/j.crad.2004.07.008 10.1109/MCSE.2007.55 10.1007/978-3-319-93000-8_61 10.1159/000341410 |
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| Keywords | Multi-center data set Feature extraction Brain image processing Machine learning Carotid artery atherosclerotic disease |
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| References | der Walt, Schonberger, Iglesias, Boulogne, Warner, Yager (bb0265) 2014; 2 Yichuan (bb0205) 2013 Yamashita, Nishio, Do, Togashi (bb0290) 2018; 9 Leite, Rittner, Appenzeller, Ruocco, Lotufo (bb0035) 2015; 2 Sandeep, Patnaik, Jagannathan (bb0190) 2006; 1 Kim, Magnotta, Liu, Johnson (bb0055) 2014; 32 Kohavi (bb0225) 1995; Vol. 2 Belaroussi, Milles, Carme, Zhu, Benoit-Cattin (bb0160) 2006; 10 Leite, Rittner, Lapa, Appenzeler, Lotufo (bb0020) 2014 Han, Kamber (bb0235) 2006 Flaherty, Kissela, Khoury, Alwell, Moomaw, Woo (bb0010) 2012; 40 Ammirati, Moroni, Magnoni, Rocca, Messina, Anzalone (bb0015) 2017; 7 Heinen, Bouvy, Mendrik, Viergever, Biessels, Bresser (bb0065) 2016; 11 Tardif, Spence, Heinonen, Moody, Pressacco, Frayne (bb0120) 2013; 29 Castellano, Bonilha, Cendes (bb0100) 2004; 59 Bergstra, Bengio (bb0215) 2012; 13 Bento, Souza, Salluzzi, Frayne (bb0175) 2018 Tofts, Collins (bb0040) 2011; 84 Kashif, Raza, Sirinukunwattana, Arif, Rajpoot (bb0095) 2016 Lu, Gobbi, Salluzzi (bb0110) 2014 Kuijf, Biesbroek, de Bresser, Heinen, Andermatt, Bento (bb0135) 2019 Bento, Souto, Salluzzi, Zhang, Frayne (bb0200) 2018 Dunnett (bb0250) 1955; 50 Pedregosa, Varoquaux, Gramfort, Michel, Thirion, Grisel (bb0210) 2011; 12 Moeskops, de Bresser, Kuijf, Mendrik, Biessels, Pluim (bb0075) 2018; 17 Bishop (bb0220) 2007 Chalavi, Simmons, Dijkstra, Barker, Reinders (bb0045) 2012; 12 Davids, Zollner, Ruttorf, Nees, Flor, Schumann (bb0050) 2014; 32 Chollet (bb0270) 2015 Bento, Souza, Frayne, Rittner (bb0170) 2017 Leite, Gobbi, Salluzi, Frayne, Lotufo, Rittner (bb0030) 2016 Kim, MacFall, Payne (bb0145) 2009; 64 Woods, Gonzalez (bb0180) 2000 Komishke, Metz, Brown, Zhang (bb0125) 2014 He, Wang (bb0185) 1990; 28 Jenkinson, Beckmann, Behrens, Woolrich, Smith (bb0165) 2012; 62 Collewet, Strzelecki, Mariette (bb0085) 2004; 22 Pearson (bb0240) 1901; 2 Friedman (bb0255) 1940; 11 Nelson, Swallen, Chung, Piper (bb0060) 2016; 57 Winkler, Hays (bb0245) 1975 Kluyver, Ragan-Kelley, Pérez, Granger, Bussonnier, Frederic (bb0260) 2016 Tsang, Lebel, Bray, Goodyear, Hafeez, Sotero (bb0140) 2017; 9 Saba, Yuan, Hatsukami, Balu, Qiao, DeMarco (bb0005) 2018 Tustison, Avants, Cook, Zheng, Egan, Yushkevich (bb0155) 2010; 29 Griffanti, Zamboni, Khan, Li, Bonifacio, Sundaresan (bb0115) 2016; 141 Wardlaw, Smith, Biessels, Cordonnier, Fazekas, Frayne (bb0130) 2013; 12 Leite, Rittner, Gobbi, Saluzzi, Frayne, Lotufo (bb0150) 2015 Breiman, Friedman, Olshen, Stone (bb0230) 1984 Schwartz, Siqueira, Pedrini (bb0285) 2012; 21 Litjens, Kooi, Bejnordi, Setio, Ciompi, Ghafoorian (bb0025) 2017; 42 Shen, Wu, Suk (bb0090) 2017; 19 Simonyan, Zisserman (bb0195) 2015 Shin, Roth, Gao, Lu, Xu, Nogues (bb0105) 2016; 35 Loizou, Pantziaris, Pattichis, Seimenis (bb0080) 2013; 3 Nieuwenhuis, Schnack, Haren, Lappin, Morgan, Reinders (bb0070) 2017; 145 Hunter (bb0275) 2007; 9 Helmer, Chou, Preciado, Gimi, N., Song (bb0280) 2016 Ammirati (10.1016/j.mri.2019.06.007_bb0015) 2017; 7 Yamashita (10.1016/j.mri.2019.06.007_bb0290) 2018; 9 Leite (10.1016/j.mri.2019.06.007_bb0035) 2015; 2 Jenkinson (10.1016/j.mri.2019.06.007_bb0165) 2012; 62 Sandeep (10.1016/j.mri.2019.06.007_bb0190) 2006; 1 Dunnett (10.1016/j.mri.2019.06.007_bb0250) 1955; 50 Belaroussi (10.1016/j.mri.2019.06.007_bb0160) 2006; 10 Heinen (10.1016/j.mri.2019.06.007_bb0065) 2016; 11 Saba (10.1016/j.mri.2019.06.007_bb0005) 2018 Litjens (10.1016/j.mri.2019.06.007_bb0025) 2017; 42 Bergstra (10.1016/j.mri.2019.06.007_bb0215) 2012; 13 Lu (10.1016/j.mri.2019.06.007_bb0110) 2014 He (10.1016/j.mri.2019.06.007_bb0185) 1990; 28 Davids (10.1016/j.mri.2019.06.007_bb0050) 2014; 32 Chollet (10.1016/j.mri.2019.06.007_bb0270) Nelson (10.1016/j.mri.2019.06.007_bb0060) 2016; 57 Han (10.1016/j.mri.2019.06.007_bb0235) 2006 Schwartz (10.1016/j.mri.2019.06.007_bb0285) 2012; 21 Griffanti (10.1016/j.mri.2019.06.007_bb0115) 2016; 141 Bento (10.1016/j.mri.2019.06.007_bb0175) 2018 Loizou (10.1016/j.mri.2019.06.007_bb0080) 2013; 3 Wardlaw (10.1016/j.mri.2019.06.007_bb0130) 2013; 12 Moeskops (10.1016/j.mri.2019.06.007_bb0075) 2018; 17 Shen (10.1016/j.mri.2019.06.007_bb0090) 2017; 19 Helmer (10.1016/j.mri.2019.06.007_bb0280) 2016 Komishke (10.1016/j.mri.2019.06.007_bb0125) 2014 Leite (10.1016/j.mri.2019.06.007_bb0030) 2016 Kluyver (10.1016/j.mri.2019.06.007_bb0260) 2016 Kim (10.1016/j.mri.2019.06.007_bb0145) 2009; 64 Breiman (10.1016/j.mri.2019.06.007_bb0230) 1984 Pearson (10.1016/j.mri.2019.06.007_bb0240) 1901; 2 Shin (10.1016/j.mri.2019.06.007_bb0105) 2016; 35 Kashif (10.1016/j.mri.2019.06.007_bb0095) 2016 Kim (10.1016/j.mri.2019.06.007_bb0055) 2014; 32 Kuijf (10.1016/j.mri.2019.06.007_bb0135) 2019 Pedregosa (10.1016/j.mri.2019.06.007_bb0210) 2011; 12 Hunter (10.1016/j.mri.2019.06.007_bb0275) 2007; 9 Woods (10.1016/j.mri.2019.06.007_bb0180) 2000 Collewet (10.1016/j.mri.2019.06.007_bb0085) 2004; 22 Kohavi (10.1016/j.mri.2019.06.007_bb0225) 1995; Vol. 2 Bishop (10.1016/j.mri.2019.06.007_bb0220) 2007 Chalavi (10.1016/j.mri.2019.06.007_bb0045) 2012; 12 Castellano (10.1016/j.mri.2019.06.007_bb0100) 2004; 59 Leite (10.1016/j.mri.2019.06.007_bb0150) 2015 Friedman (10.1016/j.mri.2019.06.007_bb0255) 1940; 11 Bento (10.1016/j.mri.2019.06.007_bb0200) 2018 Simonyan (10.1016/j.mri.2019.06.007_bb0195) 2015 Flaherty (10.1016/j.mri.2019.06.007_bb0010) 2012; 40 Tustison (10.1016/j.mri.2019.06.007_bb0155) 2010; 29 Bento (10.1016/j.mri.2019.06.007_bb0170) 2017 Tsang (10.1016/j.mri.2019.06.007_bb0140) 2017; 9 Tofts (10.1016/j.mri.2019.06.007_bb0040) 2011; 84 Winkler (10.1016/j.mri.2019.06.007_bb0245) 1975 Leite (10.1016/j.mri.2019.06.007_bb0020) 2014 Tardif (10.1016/j.mri.2019.06.007_bb0120) 2013; 29 Yichuan (10.1016/j.mri.2019.06.007_bb0205) 2013 Nieuwenhuis (10.1016/j.mri.2019.06.007_bb0070) 2017; 145 der Walt (10.1016/j.mri.2019.06.007_bb0265) 2014; 2 |
| References_xml | – year: 2016 ident: bb0095 article-title: Handcrafted features with convolutional neural networks for detection of tumor cells in histology images publication-title: Proceedings of IEEE 13th International Symposium on Biomedical Imaging – volume: 29 start-page: 1310 year: 2010 end-page: 1320 ident: bb0155 article-title: N4ITK: improved N3 bias correction publication-title: IEEE Trans Med Imaging – volume: 7 start-page: 1 year: 2017 end-page: 11 ident: bb0015 article-title: Relation between characteristics of carotid atherosclerotic plaques and brain white matter hyperintensities in asymptomatic patients publication-title: Sci Rep – volume: 9 start-page: 1 year: 2017 end-page: 13 ident: bb0140 article-title: White matter structural connectivity is not correlated to cortical resting-state functional connectivity over the healthy adult lifespan publication-title: Front Aging Neurosci – volume: 17 start-page: 251 year: 2018 end-page: 262 ident: bb0075 article-title: Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in mri publication-title: NeuroImage: Clinical – year: 2017 ident: bb0170 article-title: WMH segmentation challenge: a texture-based classification approach publication-title: Lecture Notes in Computer Science: International MICCAI Brain Lesion Workshop – year: 2015 ident: bb0150 article-title: Influence of MR image intensity normalization on texture-based classification of brain white matter lesions publication-title: Proceedings of the Second Brain Congress – volume: 3 start-page: 20 year: 2013 ident: bb0080 article-title: Brain MR image normalization in texture analysis of multiple sclerosis publication-title: Journal of Biomedical Graphics and Computing – volume: 42 start-page: 60 year: 2017 end-page: 88 ident: bb0025 article-title: A survey on deep learning in medical image analysis publication-title: Med Image Anal – volume: 11 start-page: 86 year: 1940 end-page: 92 ident: bb0255 article-title: A comparison of alternative tests of significance for the problem of m rankings publication-title: Ann Math Stat – volume: 9 start-page: 90 year: 2007 end-page: 95 ident: bb0275 article-title: Matplotlib: A 2d graphics environment publication-title: Computing In Science & Engineering – volume: 141 start-page: 191 year: 2016 end-page: 205 ident: bb0115 article-title: BIANCA (Brain Intensity Abnormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities publication-title: NeuroImage – year: 2013 ident: bb0205 article-title: Deep learning using linear support vector machines publication-title: Proceedings of International Conference on Machine Learning – volume: 13 start-page: 281 year: 2012 end-page: 305 ident: bb0215 article-title: Random search for hyper-parameter optimization publication-title: Journal Machine Learning Research – volume: 9 start-page: 611 year: 2018 end-page: 629 ident: bb0290 article-title: Convolutional neural networks: an overview and application in radiology publication-title: Insights into Imaging – volume: 62 start-page: 782 year: 2012 end-page: 790 ident: bb0165 article-title: FSL publication-title: Neuroimage – volume: 19 start-page: 221 year: 2017 end-page: 248 ident: bb0090 article-title: Deep learning in medical image analysis publication-title: Annu Rev Biomed Eng – volume: 11 year: 2016 ident: bb0065 article-title: Robustness of automated methods for brain volume measurements across different MRI field strengths publication-title: Plos One – volume: 2 start-page: 301 year: 1901 ident: bb0240 article-title: On lines and planes of closest fit to systems of points in space publication-title: Philosophical Magazine – year: 2016 ident: bb0030 article-title: 3D texture-based classification applied on brain white matter lesions on MR images publication-title: Proceedings Volume 9785: Medical Imaging 2016: Computer-aided Diagnosis SPIE – volume: 21 start-page: 1 year: 2012 end-page: 17 ident: bb0285 article-title: Evaluation of feature descriptors for texture classification publication-title: Journal of Eletronic Imaging – start-page: 1 year: 2018 end-page: 23 ident: bb0005 article-title: Carotid artery wall imaging: perspective and guidelines from the ASNR vessel wall imaging study group and expert consensus recommendations of the American Society of Neuroradiology publication-title: Am J Neuroradiol – year: 2019 ident: bb0135 article-title: Standardized assessment of automatic segmentation of white matter hyperintensities; results of the wmh segmentation challenge publication-title: IEEE Trans Med Imaging – year: 1975 ident: bb0245 article-title: Statistics: probability, inference, and decision – start-page: 87 year: 2016 end-page: 90 ident: bb0260 article-title: Jupyter notebooks – a publishing format for reproducible computational workflows publication-title: Positioning and power in academic publishing: players, agents and agendas – volume: 32 start-page: 832 year: 2014 end-page: 844 ident: bb0055 article-title: Stable atlas-based mapped prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data publication-title: Magn Reson Imaging – year: 2018 ident: bb0175 article-title: Normal brain aging: prediction of age, sex and white matter hyperintensities using a MR image-based machine learning technique publication-title: Lecture Notes in Computer Science: International Conference Image Analysis and Recognition – year: 2015 ident: bb0270 article-title: Keras – volume: 57 start-page: 551 year: 2016 ident: bb0060 article-title: Evaluation of a quantitative metric, volumetric statistical amyloid burden (VSAB), for Florbetapir PET, to classify amyloid positive and negative subjects using a cutoff derived from an independent dataset publication-title: The Journal of Nuclear Medicine – volume: 84 start-page: 213 year: 2011 end-page: 226 ident: bb0040 article-title: Multicentre imaging measurements for oncology and in the brain publication-title: Br J Radiol – volume: 145 start-page: 246 year: 2017 end-page: 253 ident: bb0070 article-title: Multi-center MRI prediction models: predicting sex and illness course in first episode psychosis patients publication-title: Neuroimage – year: 2000 ident: bb0180 article-title: Digital image processing – volume: 50 start-page: 1096 year: 1955 end-page: 1121 ident: bb0250 article-title: A multiple comparisons procedure for comparing several treatments with a control publication-title: J Am Stat Assoc – volume: 40 start-page: 36 year: 2012 end-page: 41 ident: bb0010 article-title: Carotid artery stenosis as a cause of stroke publication-title: Neuroepidemiology – volume: 35 start-page: 1285 year: 2016 end-page: 1298 ident: bb0105 article-title: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning publication-title: IEEE Trans Med Imaging – volume: 28 start-page: 509 year: 1990 end-page: 512 ident: bb0185 article-title: Texture unit, texture spectrum, and texture analysis publication-title: IEEE Transactions on Geoscience and Remote Sensing – volume: 12 start-page: 1 year: 2012 end-page: 15 ident: bb0045 article-title: Quantitative and qualitative assessment of structural magnetic resonance imaging data in a two-center study publication-title: BMC Med Imaging – volume: 59 start-page: 1061 year: 2004 end-page: 1069 ident: bb0100 article-title: Texture analysis of medical images publication-title: Clin Radiol – volume: 12 start-page: 2825 year: 2011 end-page: 2830 ident: bb0210 article-title: Scikit-learn: machine learning in Python publication-title: Journal of Machine Learning Research – volume: 22 start-page: 81 year: 2004 end-page: 91 ident: bb0085 article-title: Influence of MRI acquisition protocols and image intensity normalization methods on texture classification publication-title: Magn Reson Imaging – year: 2018 ident: bb0200 article-title: Feature extraction using convolutional networks for identifying carotid artery atherosclerosis patients in a heterogeneous brain MR dataset publication-title: Proceedings of Joint Annual Meeting of International Society for Magnetic Resonance in Medicine – volume: 29 start-page: 297 year: 2013 end-page: 303 ident: bb0120 article-title: Atherosclerosis imaging and the Canadian Atherosclerosis Imaging Network publication-title: Can J Cardiol – volume: 64 start-page: 273 year: 2009 end-page: 280 ident: bb0145 article-title: Classification of white matter lesions on magnetic resonance imaging in the elderly publication-title: Biol Psychiatry – volume: Vol. 2 start-page: 1137 year: 1995 end-page: 1143 ident: bb0225 article-title: A study of cross-validation and bootstrap for accuracy estimation and model selection publication-title: Proceedings of the 14th International Joint Conference on Artificial Intelligence – year: 2015 ident: bb0195 article-title: Very deep convolutional networks for large-scale image recognition publication-title: Proceedings of 3rd International Conference on Learning Representations – volume: 32 start-page: 771 year: 2014 end-page: 780 ident: bb0050 article-title: Fully-automated quality assurance in multi-center studies using MRI phantom measurements publication-title: Magn Reson Imaging – volume: 12 start-page: 822 year: 2013 end-page: 838 ident: bb0130 article-title: Neuroimaging standards for research into small vessel disease and its contribution to aging and neurodegeneration publication-title: Lancet Neurol – year: 2007 ident: bb0220 article-title: Pattern recognition and machine learning – year: 2016 ident: bb0280 article-title: Multi-site study of diffusion metric variability: effects of site, vendor, field strength, and echo time on regions-of-interest and histogram-bin analyses publication-title: Proceedings of SPIE–the International Society for Optical Engineering – year: 2014 ident: bb0020 article-title: Classification of brain white matter lesion on MR imaging publication-title: Proceedings of the 15th Annual Alberta Biomedical Engineering Conference – year: 1984 ident: bb0230 article-title: Classification and regression trees – year: 2006 ident: bb0235 article-title: Data mining: concepts and techniques – volume: 2 year: 2015 ident: bb0035 article-title: Etiology-based classification of brain white matter hyperintensity on magnetic resonance imaging publication-title: Journal of Medical Imaging – year: 2014 ident: bb0110 article-title: Cerebra-WML: a stand-alone application for quantification of white matter lesion publication-title: Proceedings of Imaging Network Ontario Symposium – volume: 2 start-page: e453 year: 2014 ident: bb0265 article-title: Scikit-image: image processing in python publication-title: PeerJ the Journal of Life and Environmental Sciences – volume: 10 start-page: 234 year: 2006 end-page: 246 ident: bb0160 article-title: Intensity nonuniformity correction in MRI: existing methods and their validation publication-title: Med Image Anal – year: 2014 ident: bb0125 article-title: Preferably large fiber damage in the corpus callosum of progressive MS compared with relapsing MS and controls publication-title: Proceedings of the 22nd International Society for Magnetic Resonance in Medicine – volume: 1 start-page: 86 year: 2006 end-page: 92 ident: bb0190 article-title: Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network publication-title: Biomedical Signal Processing and Control – year: 2016 ident: 10.1016/j.mri.2019.06.007_bb0095 article-title: Handcrafted features with convolutional neural networks for detection of tumor cells in histology images – volume: 2 issue: 1 year: 2015 ident: 10.1016/j.mri.2019.06.007_bb0035 article-title: Etiology-based classification of brain white matter hyperintensity on magnetic resonance imaging publication-title: Journal of Medical Imaging doi: 10.1117/1.JMI.2.1.014002 – volume: Vol. 2 start-page: 1137 year: 1995 ident: 10.1016/j.mri.2019.06.007_bb0225 article-title: A study of cross-validation and bootstrap for accuracy estimation and model selection – year: 2019 ident: 10.1016/j.mri.2019.06.007_bb0135 article-title: Standardized assessment of automatic segmentation of white matter hyperintensities; results of the wmh segmentation challenge publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2019.2905770 – volume: 11 issue: 10 year: 2016 ident: 10.1016/j.mri.2019.06.007_bb0065 article-title: Robustness of automated methods for brain volume measurements across different MRI field strengths publication-title: Plos One doi: 10.1371/journal.pone.0165719 – volume: 32 start-page: 771 issue: 6 year: 2014 ident: 10.1016/j.mri.2019.06.007_bb0050 article-title: Fully-automated quality assurance in multi-center studies using MRI phantom measurements publication-title: Magn Reson Imaging doi: 10.1016/j.mri.2014.01.017 – volume: 50 start-page: 1096 issue: 272 year: 1955 ident: 10.1016/j.mri.2019.06.007_bb0250 article-title: A multiple comparisons procedure for comparing several treatments with a control publication-title: J Am Stat Assoc doi: 10.1080/01621459.1955.10501294 – year: 2015 ident: 10.1016/j.mri.2019.06.007_bb0150 article-title: Influence of MR image intensity normalization on texture-based classification of brain white matter lesions – year: 2013 ident: 10.1016/j.mri.2019.06.007_bb0205 article-title: Deep learning using linear support vector machines – year: 1975 ident: 10.1016/j.mri.2019.06.007_bb0245 – start-page: 1 year: 2018 ident: 10.1016/j.mri.2019.06.007_bb0005 article-title: Carotid artery wall imaging: perspective and guidelines from the ASNR vessel wall imaging study group and expert consensus recommendations of the American Society of Neuroradiology publication-title: Am J Neuroradiol – year: 2017 ident: 10.1016/j.mri.2019.06.007_bb0170 article-title: WMH segmentation challenge: a texture-based classification approach doi: 10.1007/978-3-319-67810-8 – volume: 84 start-page: 213 year: 2011 ident: 10.1016/j.mri.2019.06.007_bb0040 article-title: Multicentre imaging measurements for oncology and in the brain publication-title: Br J Radiol doi: 10.1259/bjr/74316620 – volume: 10 start-page: 234 issue: 2 year: 2006 ident: 10.1016/j.mri.2019.06.007_bb0160 article-title: Intensity nonuniformity correction in MRI: existing methods and their validation publication-title: Med Image Anal doi: 10.1016/j.media.2005.09.004 – volume: 1 start-page: 86 issue: 1 year: 2006 ident: 10.1016/j.mri.2019.06.007_bb0190 article-title: Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2006.05.002 – year: 2014 ident: 10.1016/j.mri.2019.06.007_bb0020 article-title: Classification of brain white matter lesion on MR imaging – volume: 19 start-page: 221 year: 2017 ident: 10.1016/j.mri.2019.06.007_bb0090 article-title: Deep learning in medical image analysis publication-title: Annu Rev Biomed Eng doi: 10.1146/annurev-bioeng-071516-044442 – volume: 12 start-page: 2825 issue: 1 year: 2011 ident: 10.1016/j.mri.2019.06.007_bb0210 article-title: Scikit-learn: machine learning in Python publication-title: Journal of Machine Learning Research – year: 2014 ident: 10.1016/j.mri.2019.06.007_bb0125 article-title: Preferably large fiber damage in the corpus callosum of progressive MS compared with relapsing MS and controls – volume: 22 start-page: 81 issue: 1 year: 2004 ident: 10.1016/j.mri.2019.06.007_bb0085 article-title: Influence of MRI acquisition protocols and image intensity normalization methods on texture classification publication-title: Magn Reson Imaging doi: 10.1016/j.mri.2003.09.001 – volume: 32 start-page: 832 issue: 7 year: 2014 ident: 10.1016/j.mri.2019.06.007_bb0055 article-title: Stable atlas-based mapped prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data publication-title: Magn Reson Imaging doi: 10.1016/j.mri.2014.04.016 – volume: 145 start-page: 246 issue: Pt B year: 2017 ident: 10.1016/j.mri.2019.06.007_bb0070 article-title: Multi-center MRI prediction models: predicting sex and illness course in first episode psychosis patients publication-title: Neuroimage doi: 10.1016/j.neuroimage.2016.07.027 – volume: 3 start-page: 20 issue: 1 year: 2013 ident: 10.1016/j.mri.2019.06.007_bb0080 article-title: Brain MR image normalization in texture analysis of multiple sclerosis publication-title: Journal of Biomedical Graphics and Computing – volume: 11 start-page: 86 issue: 1 year: 1940 ident: 10.1016/j.mri.2019.06.007_bb0255 article-title: A comparison of alternative tests of significance for the problem of m rankings publication-title: Ann Math Stat doi: 10.1214/aoms/1177731944 – volume: 9 start-page: 611 issue: 4 year: 2018 ident: 10.1016/j.mri.2019.06.007_bb0290 article-title: Convolutional neural networks: an overview and application in radiology publication-title: Insights into Imaging doi: 10.1007/s13244-018-0639-9 – volume: 2 start-page: e453 year: 2014 ident: 10.1016/j.mri.2019.06.007_bb0265 article-title: Scikit-image: image processing in python publication-title: PeerJ the Journal of Life and Environmental Sciences – year: 2000 ident: 10.1016/j.mri.2019.06.007_bb0180 – volume: 42 start-page: 60 issue: Supplement C year: 2017 ident: 10.1016/j.mri.2019.06.007_bb0025 article-title: A survey on deep learning in medical image analysis publication-title: Med Image Anal doi: 10.1016/j.media.2017.07.005 – volume: 141 start-page: 191 year: 2016 ident: 10.1016/j.mri.2019.06.007_bb0115 article-title: BIANCA (Brain Intensity Abnormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities publication-title: NeuroImage doi: 10.1016/j.neuroimage.2016.07.018 – ident: 10.1016/j.mri.2019.06.007_bb0270 – volume: 17 start-page: 251 year: 2018 ident: 10.1016/j.mri.2019.06.007_bb0075 article-title: Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in mri publication-title: NeuroImage: Clinical doi: 10.1016/j.nicl.2017.10.007 – year: 2016 ident: 10.1016/j.mri.2019.06.007_bb0030 article-title: 3D texture-based classification applied on brain white matter lesions on MR images – volume: 21 start-page: 1 issue: 2 year: 2012 ident: 10.1016/j.mri.2019.06.007_bb0285 article-title: Evaluation of feature descriptors for texture classification publication-title: Journal of Eletronic Imaging – volume: 35 start-page: 1285 issue: 5 year: 2016 ident: 10.1016/j.mri.2019.06.007_bb0105 article-title: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2016.2528162 – year: 2016 ident: 10.1016/j.mri.2019.06.007_bb0280 article-title: Multi-site study of diffusion metric variability: effects of site, vendor, field strength, and echo time on regions-of-interest and histogram-bin analyses – year: 2014 ident: 10.1016/j.mri.2019.06.007_bb0110 article-title: Cerebra-WML: a stand-alone application for quantification of white matter lesion – volume: 62 start-page: 782 year: 2012 ident: 10.1016/j.mri.2019.06.007_bb0165 article-title: FSL publication-title: Neuroimage doi: 10.1016/j.neuroimage.2011.09.015 – volume: 12 start-page: 1 issue: 27 year: 2012 ident: 10.1016/j.mri.2019.06.007_bb0045 article-title: Quantitative and qualitative assessment of structural magnetic resonance imaging data in a two-center study publication-title: BMC Med Imaging – volume: 13 start-page: 281 issue: 1 year: 2012 ident: 10.1016/j.mri.2019.06.007_bb0215 article-title: Random search for hyper-parameter optimization publication-title: Journal Machine Learning Research – year: 2018 ident: 10.1016/j.mri.2019.06.007_bb0200 article-title: Feature extraction using convolutional networks for identifying carotid artery atherosclerosis patients in a heterogeneous brain MR dataset – volume: 12 start-page: 822 year: 2013 ident: 10.1016/j.mri.2019.06.007_bb0130 article-title: Neuroimaging standards for research into small vessel disease and its contribution to aging and neurodegeneration publication-title: Lancet Neurol doi: 10.1016/S1474-4422(13)70124-8 – volume: 29 start-page: 297 issue: 3 year: 2013 ident: 10.1016/j.mri.2019.06.007_bb0120 article-title: Atherosclerosis imaging and the Canadian Atherosclerosis Imaging Network publication-title: Can J Cardiol – volume: 29 start-page: 1310 issue: 6 year: 2010 ident: 10.1016/j.mri.2019.06.007_bb0155 article-title: N4ITK: improved N3 bias correction publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2010.2046908 – start-page: 87 year: 2016 ident: 10.1016/j.mri.2019.06.007_bb0260 article-title: Jupyter notebooks – a publishing format for reproducible computational workflows – volume: 7 start-page: 1 issue: 10559 year: 2017 ident: 10.1016/j.mri.2019.06.007_bb0015 article-title: Relation between characteristics of carotid atherosclerotic plaques and brain white matter hyperintensities in asymptomatic patients publication-title: Sci Rep – year: 2015 ident: 10.1016/j.mri.2019.06.007_bb0195 article-title: Very deep convolutional networks for large-scale image recognition – volume: 9 start-page: 1 issue: 144 year: 2017 ident: 10.1016/j.mri.2019.06.007_bb0140 article-title: White matter structural connectivity is not correlated to cortical resting-state functional connectivity over the healthy adult lifespan publication-title: Front Aging Neurosci – volume: 2 start-page: 301 issue: 11 year: 1901 ident: 10.1016/j.mri.2019.06.007_bb0240 article-title: On lines and planes of closest fit to systems of points in space publication-title: Philosophical Magazine – year: 2006 ident: 10.1016/j.mri.2019.06.007_bb0235 – volume: 59 start-page: 1061 issue: 12 year: 2004 ident: 10.1016/j.mri.2019.06.007_bb0100 article-title: Texture analysis of medical images publication-title: Clin Radiol doi: 10.1016/j.crad.2004.07.008 – volume: 28 start-page: 509 issue: 1 year: 1990 ident: 10.1016/j.mri.2019.06.007_bb0185 article-title: Texture unit, texture spectrum, and texture analysis publication-title: IEEE Transactions on Geoscience and Remote Sensing – year: 2007 ident: 10.1016/j.mri.2019.06.007_bb0220 – volume: 57 start-page: 551 issue: 2 year: 2016 ident: 10.1016/j.mri.2019.06.007_bb0060 article-title: Evaluation of a quantitative metric, volumetric statistical amyloid burden (VSAB), for Florbetapir PET, to classify amyloid positive and negative subjects using a cutoff derived from an independent dataset publication-title: The Journal of Nuclear Medicine – volume: 9 start-page: 90 issue: 3 year: 2007 ident: 10.1016/j.mri.2019.06.007_bb0275 article-title: Matplotlib: A 2d graphics environment publication-title: Computing In Science & Engineering doi: 10.1109/MCSE.2007.55 – year: 2018 ident: 10.1016/j.mri.2019.06.007_bb0175 article-title: Normal brain aging: prediction of age, sex and white matter hyperintensities using a MR image-based machine learning technique doi: 10.1007/978-3-319-93000-8_61 – volume: 40 start-page: 36 issue: 1 year: 2012 ident: 10.1016/j.mri.2019.06.007_bb0010 article-title: Carotid artery stenosis as a cause of stroke publication-title: Neuroepidemiology doi: 10.1159/000341410 – volume: 64 start-page: 273 issue: 1 year: 2009 ident: 10.1016/j.mri.2019.06.007_bb0145 article-title: Classification of white matter lesions on magnetic resonance imaging in the elderly publication-title: Biol Psychiatry – year: 1984 ident: 10.1016/j.mri.2019.06.007_bb0230 |
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| Snippet | Carotid-artery atherosclerosis (CA) contributes significantly to overall morbidity and mortality in ischemic stroke. We propose a machine learning technique to... |
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| SubjectTerms | Adult Aged Aged, 80 and over Atherosclerosis - diagnostic imaging Atherosclerosis - pathology Brain - diagnostic imaging Brain - pathology Brain image processing Carotid Arteries - diagnostic imaging Carotid artery atherosclerotic disease Cohort Studies Feature extraction Female Humans Machine learning Magnetic Resonance Imaging Male Middle Aged Multi-center data set Multiple Sclerosis - diagnostic imaging Neuroimaging Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Support Vector Machine White Matter - diagnostic imaging |
| Title | Automatic identification of atherosclerosis subjects in a heterogeneous MR brain imaging data set |
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