White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts – The MRI-GENIE study
White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delinea...
Saved in:
| Published in | NeuroImage clinical Vol. 23; p. 101884 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier Inc
01.01.2019
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2213-1582 2213-1582 |
| DOI | 10.1016/j.nicl.2019.101884 |
Cover
| Abstract | White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery.
In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94–0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of −2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing.
Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited.
•Fully automated high-throughput white matter hyperintensity segmentation pipeline.•Methodology designed for and applied to international clinical multi-site data.•Calculation of disease burden in 2533 acute ischemic stroke patients.•Total brain volume change with age (−2.4 cc/year) used in automated quality control.•Increase of white matter hyperintensity burden of 0.95 cc/year. |
|---|---|
| AbstractList | White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94-0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of -2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited. White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94–0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of −2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited. •Fully automated high-throughput white matter hyperintensity segmentation pipeline.•Methodology designed for and applied to international clinical multi-site data.•Calculation of disease burden in 2533 acute ischemic stroke patients.•Total brain volume change with age (−2.4 cc/year) used in automated quality control.•Increase of white matter hyperintensity burden of 0.95 cc/year. White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94–0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of −2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited. White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94–0.95; p < 0.01) and Pearson correlation of total brain volume ( r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study ( N = 2783) and identify a decrease in total brain volume of −2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited. • Fully automated high-throughput white matter hyperintensity segmentation pipeline. • Methodology designed for and applied to international clinical multi-site data. • Calculation of disease burden in 2533 acute ischemic stroke patients. • Total brain volume change with age (−2.4 cc/year) used in automated quality control. • Increase of white matter hyperintensity burden of 0.95 cc/year. AbstractWhite matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94–0.95; p < 0.01) and Pearson correlation of total brain volume ( r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study ( N = 2783) and identify a decrease in total brain volume of −2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited. White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94-0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of -2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited.White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94-0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of -2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited. |
| ArticleNumber | 101884 |
| Author | Slowik, Agnieszka Roquer, Jaume Nardin, Marco J. Worrall, Bradford B. Mocking, Steven J.T. Rost, Natalia S. McArdle, Patrick F. Jern, Christina Lindgren, Arne G. Rundek, Tatjana Mitchell, Braxton D. Donahue, Kathleen L. McIntosh, Elissa C. Jimenez-Conde, Jordi Xu, Huichun Lemmens, Robin Vagal, Achala Giese, Anne-Katrin Frid, Petrea Wasselius, Johan Cole, John W. Sharma, Pankaj Schirmer, Markus D. Sridharan, Ramesh Sacco, Ralph L. Thijs, Vincent Woo, Daniel Rosand, Jonathan Kittner, Steven J. Holmegaard, Lukas Dalca, Adrian V. Meschia, James F. Golland, Polina Schmidt, Reinhold Wu, Ona |
| AuthorAffiliation | s Institute of Cardiovascular Research, St Peter's and Ashford Hospitals, Royal Holloway University of London (ICR2UL), Egham, UK u Stroke Division, Australia and Department of Neurology, Austin Health, Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia j Institute of Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden n VIB, Vesalius Research Center, Laboratory of Neurobiology, Department of Neurology, University Hospitals Leuven, Leuven, Belgium r Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria i Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA t Department of Neurology, Jagiellonian University Medical College, Krakow, Poland p Department of Neurology, Mayo Clinic, Jacksonville, FL, USA k Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Swed |
| AuthorAffiliation_xml | – name: l Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain – name: f Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden – name: m Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), KU Leuven - University of Leuven, Leuven, Belgium – name: e Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA – name: z Departments of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, USA – name: i Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA – name: y Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA – name: j Institute of Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden – name: o Department of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund, Sweden – name: r Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria – name: b Computer Science and Artificial Intelligence Lab, MIT, USA – name: g Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden – name: k Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden – name: q Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA – name: x Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA – name: u Stroke Division, Australia and Department of Neurology, Austin Health, Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia – name: h Department of Radiology, Neuroradiology, Skåne University Hospital, Malmö, Sweden – name: s Institute of Cardiovascular Research, St Peter's and Ashford Hospitals, Royal Holloway University of London (ICR2UL), Egham, UK – name: w Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA – name: d Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA – name: n VIB, Vesalius Research Center, Laboratory of Neurobiology, Department of Neurology, University Hospitals Leuven, Leuven, Belgium – name: c Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Germany – name: p Department of Neurology, Mayo Clinic, Jacksonville, FL, USA – name: t Department of Neurology, Jagiellonian University Medical College, Krakow, Poland – name: a Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA – name: v Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA |
| Author_xml | – sequence: 1 givenname: Markus D. orcidid: 0000-0001-9561-0239 surname: Schirmer fullname: Schirmer, Markus D. email: mschirmer1@mgh.harvard.edu organization: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA – sequence: 2 givenname: Adrian V. surname: Dalca fullname: Dalca, Adrian V. organization: Computer Science and Artificial Intelligence Lab, MIT, USA – sequence: 3 givenname: Ramesh surname: Sridharan fullname: Sridharan, Ramesh organization: Computer Science and Artificial Intelligence Lab, MIT, USA – sequence: 4 givenname: Anne-Katrin surname: Giese fullname: Giese, Anne-Katrin organization: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA – sequence: 5 givenname: Kathleen L. surname: Donahue fullname: Donahue, Kathleen L. organization: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA – sequence: 6 givenname: Marco J. surname: Nardin fullname: Nardin, Marco J. organization: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA – sequence: 7 givenname: Steven J.T. surname: Mocking fullname: Mocking, Steven J.T. organization: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA – sequence: 8 givenname: Elissa C. surname: McIntosh fullname: McIntosh, Elissa C. organization: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA – sequence: 9 givenname: Petrea surname: Frid fullname: Frid, Petrea organization: Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden – sequence: 10 givenname: Johan surname: Wasselius fullname: Wasselius, Johan organization: Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden – sequence: 11 givenname: John W. surname: Cole fullname: Cole, John W. organization: Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA – sequence: 12 givenname: Lukas surname: Holmegaard fullname: Holmegaard, Lukas organization: Institute of Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden – sequence: 13 givenname: Christina surname: Jern fullname: Jern, Christina organization: Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden – sequence: 14 givenname: Jordi surname: Jimenez-Conde fullname: Jimenez-Conde, Jordi organization: Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain – sequence: 15 givenname: Robin surname: Lemmens fullname: Lemmens, Robin organization: Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), KU Leuven - University of Leuven, Leuven, Belgium – sequence: 16 givenname: Arne G. surname: Lindgren fullname: Lindgren, Arne G. organization: Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden – sequence: 17 givenname: James F. surname: Meschia fullname: Meschia, James F. organization: Department of Neurology, Mayo Clinic, Jacksonville, FL, USA – sequence: 18 givenname: Jaume surname: Roquer fullname: Roquer, Jaume organization: Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain – sequence: 19 givenname: Tatjana surname: Rundek fullname: Rundek, Tatjana organization: Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA – sequence: 20 givenname: Ralph L. surname: Sacco fullname: Sacco, Ralph L. organization: Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA – sequence: 21 givenname: Reinhold surname: Schmidt fullname: Schmidt, Reinhold organization: Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria – sequence: 22 givenname: Pankaj surname: Sharma fullname: Sharma, Pankaj organization: Institute of Cardiovascular Research, St Peter's and Ashford Hospitals, Royal Holloway University of London (ICR2UL), Egham, UK – sequence: 23 givenname: Agnieszka surname: Slowik fullname: Slowik, Agnieszka organization: Department of Neurology, Jagiellonian University Medical College, Krakow, Poland – sequence: 24 givenname: Vincent surname: Thijs fullname: Thijs, Vincent organization: Stroke Division, Australia and Department of Neurology, Austin Health, Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia – sequence: 25 givenname: Daniel surname: Woo fullname: Woo, Daniel organization: Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA – sequence: 26 givenname: Achala surname: Vagal fullname: Vagal, Achala organization: Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA – sequence: 27 givenname: Huichun surname: Xu fullname: Xu, Huichun organization: Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA – sequence: 28 givenname: Steven J. surname: Kittner fullname: Kittner, Steven J. organization: Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA – sequence: 29 givenname: Patrick F. surname: McArdle fullname: McArdle, Patrick F. organization: Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA – sequence: 30 givenname: Braxton D. surname: Mitchell fullname: Mitchell, Braxton D. organization: Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA – sequence: 31 givenname: Jonathan surname: Rosand fullname: Rosand, Jonathan organization: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA – sequence: 32 givenname: Bradford B. surname: Worrall fullname: Worrall, Bradford B. organization: Departments of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, USA – sequence: 33 givenname: Ona surname: Wu fullname: Wu, Ona organization: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA – sequence: 34 givenname: Polina surname: Golland fullname: Golland, Polina organization: Computer Science and Artificial Intelligence Lab, MIT, USA – sequence: 35 givenname: Natalia S. surname: Rost fullname: Rost, Natalia S. organization: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31200151$$D View this record in MEDLINE/PubMed https://gup.ub.gu.se/publication/284488$$DView record from Swedish Publication Index |
| BookMark | eNp9U01v1DAQjVARLaV_gAPKkUuK7Xw5HJBQtS0rFZCgiONo4kw23mbjre0U9sZ_4B_yS3C6W9Qitb7YGr95b2b8_DzaG8xAUfSSs2POePFmeTxo1R8LxqspIGX2JDoQgqcJz6XYu3Pej46cW7KwJGNlUTyL9lMuGOM5P4h-fu-0p3iF3pONu82arB48DU77TXw14uB1qxV6bYZYD3GPdkGJU9hTrHodSsA-RjUGCu1URyutYuetuQzXpjPWu_jPr9_xRUfxxy_z5Gz2aT4LgLHZvIiettg7Otrth9G309nFyYfk_PPZ_OT9eaLKQvokK-ucaqSUNVWGQmHaNqQqVtayKUtGdcawSFMVus1rLtNGoRJYpi1iLmXRpIfRfMvbGFzC2uoV2g0Y1HATMHYBaH0YJQFvec2rpuZlhVnLRS0akqouMy4zWbAycOGWy_2g9VjfY1uHZrEHS47Qqg76ERxBQPW78Tlom7JuUeYgqlRBplAAVkJCjqwpCiEpJxU0kgc1FuMaQmhxQy1klkkZ8O-2-ABeUaNo8DbUca-0ezeD7mBhrqHIC5HyIhC83hFYczWS87AKT0l9jwOZ0YEQWZhtzooqQF_d1foncmunAJBbgLLGOUstKO1vug_SugfOYDIvLGEyL0zmha15Q6r4L_WW_dGkXfMUHHStycKtKS9pQ25pRjsEcwEHJ4DB1-lPTF-CVykTaTa96NuHCYJD9GPqfwF3hiE9 |
| CitedBy_id | crossref_primary_10_1002_acn3_52215 crossref_primary_10_3389_fninf_2024_1508161 crossref_primary_10_1111_cns_14841 crossref_primary_10_1111_joim_13425 crossref_primary_10_3389_fnins_2021_691244 crossref_primary_10_1053_j_sult_2022_02_004 crossref_primary_10_3390_jcm8111823 crossref_primary_10_1089_brain_2020_0980 crossref_primary_10_3389_fnins_2022_994458 crossref_primary_10_1038_s41596_024_01085_w crossref_primary_10_1159_000509071 crossref_primary_10_1093_braincomms_fcae007 crossref_primary_10_3390_cancers13081939 crossref_primary_10_1016_j_compmedimag_2021_101867 crossref_primary_10_3389_fneur_2021_700616 crossref_primary_10_1016_j_media_2020_101698 crossref_primary_10_1093_braincomms_fcab080 crossref_primary_10_1007_s12194_023_00728_z crossref_primary_10_1017_S1355617720000533 crossref_primary_10_1002_hbm_26159 crossref_primary_10_3389_fstro_2024_1468772 crossref_primary_10_1212_WNL_0000000000200926 crossref_primary_10_1016_j_nic_2020_07_001 crossref_primary_10_1161_STROKEAHA_121_035038 crossref_primary_10_3389_fnagi_2023_1165324 crossref_primary_10_1111_irv_12917 crossref_primary_10_3389_fnins_2022_866312 crossref_primary_10_1161_STROKEAHA_119_025738 crossref_primary_10_3174_ajnr_A6357 crossref_primary_10_1016_j_jpsychores_2022_110973 crossref_primary_10_1161_STROKEAHA_120_033014 crossref_primary_10_3174_ajnr_A7302 crossref_primary_10_1161_STROKEAHA_120_030260 crossref_primary_10_1161_JAHA_123_034162 crossref_primary_10_1093_brain_awab439 crossref_primary_10_1002_hbm_25899 crossref_primary_10_1016_j_mayocp_2020_01_027 crossref_primary_10_1212_WNL_0000000000009728 crossref_primary_10_3389_fneur_2020_00577 crossref_primary_10_1212_WNL_0000000000201398 crossref_primary_10_1212_WNL_0000000000201596 crossref_primary_10_1016_S1474_4422_23_00131_X crossref_primary_10_3389_fnimg_2023_1099301 |
| Cites_doi | 10.1212/NXG.0000000000000180 10.1016/S1474-4422(13)70124-8 10.1016/j.neuroimage.2017.06.009 10.1212/01.wnl.0000223613.57229.24 10.1016/j.jstrokecerebrovasdis.2014.10.016 10.1109/TMI.2011.2138152 10.1136/bmj.c3666 10.1109/34.400568 10.1161/01.STR.0000150668.58689.f2 10.1002/hbm.10062 10.2307/1932409 10.1212/WNL.0000000000003890 10.1161/STROKEAHA.110.596056 10.1161/01.STR.0000129643.77045.10 10.1016/j.neuroimage.2010.09.025 10.1016/j.atherosclerosis.2015.02.052 10.1186/1471-2342-12-17 10.7717/peerj.453 10.1007/s12021-015-9260-y 10.1186/s12880-015-0068-x |
| ContentType | Journal Article |
| Copyright | 2019 The Authors The Authors Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved. 2019 The Authors 2019 |
| Copyright_xml | – notice: 2019 The Authors – notice: The Authors – notice: Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved. – notice: 2019 The Authors 2019 |
| CorporateAuthor | on behalf of the MRI-GENIE Investigators MRI-GENIE Investigators Clinical Stroke Research Group Section V Diagnostic Radiology, (Lund) Institutionen för kliniska vetenskaper, Lund Lunds universitet Profile areas and other strong research environments Lund University Sektion V Neuroradiology Department of Clinical Sciences, Lund Neurology, Lund Diagnostisk radiologi, Lund Strategiska forskningsområden (SFO) EpiHealth: Epidemiology for Health Faculty of Medicine Strategic research areas (SRA) Klinisk strokeforskning Section IV Medicinska fakulteten Sektion IV Profilområden och andra starka forskningsmiljöer Neurologi, Lund Neuroradiologi |
| CorporateAuthor_xml | – name: on behalf of the MRI-GENIE Investigators – name: MRI-GENIE Investigators – name: Faculty of Medicine – name: Medicinska fakulteten – name: Strategiska forskningsområden (SFO) – name: Clinical Stroke Research Group – name: EpiHealth: Epidemiology for Health – name: Institutionen för kliniska vetenskaper, Lund – name: Diagnostisk radiologi, Lund – name: Strategic research areas (SRA) – name: Neurology, Lund – name: Lunds universitet – name: Diagnostic Radiology, (Lund) – name: Department of Clinical Sciences, Lund – name: Neuroradiologi – name: Sektion IV – name: Profilområden och andra starka forskningsmiljöer – name: Lund University – name: Section IV – name: Neurologi, Lund – name: Sektion V – name: Section V – name: Neuroradiology – name: Profile areas and other strong research environments – name: Klinisk strokeforskning |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM ADTPV AOWAS F1U D95 DOA |
| DOI | 10.1016/j.nicl.2019.101884 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic PubMed Central (Full Participant titles) SwePub SwePub Articles SWEPUB Göteborgs universitet SWEPUB Lunds universitet DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2213-1582 |
| EndPage | 101884 |
| ExternalDocumentID | oai_doaj_org_article_1f1b19db179a4f12b2de8cb741848607 oai_portal_research_lu_se_publications_fd7bfa85_293c_4ca2_a928_5a0d6628e5ec oai_gup_ub_gu_se_284488 PMC6562316 31200151 10_1016_j_nicl_2019_101884 1_s2_0_S2213158219302347 S2213158219302347 |
| Genre | Research Support, Non-U.S. Gov't Journal Article Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: NINDS NIH HHS grantid: R01 NS100178 – fundername: NIDDK NIH HHS grantid: P30 DK072488 |
| GroupedDBID | .1- .FO 0R~ 1P~ 457 53G 5VS AAEDT AAEDW AAIKJ AALRI AAXUO AAYWO ABMAC ACGFS ACVFH ADBBV ADCNI ADEZE ADRAZ ADVLN AEUPX AEXQZ AFJKZ AFPUW AFRHN AFTJW AGHFR AIGII AITUG AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ AOIJS APXCP BAWUL BCNDV DIK EBS EJD FDB GROUPED_DOAJ HYE HZ~ IPNFZ IXB KQ8 M41 M48 M~E O-L O9- OK1 RIG ROL RPM SSZ Z5R AFCTW AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM ADTPV AOWAS F1U D95 |
| ID | FETCH-LOGICAL-c768t-47b5ebae30d94a2ca3fdec907b8d770eb40a633c2215b183dcac2a73faa5886d3 |
| IEDL.DBID | M48 |
| ISSN | 2213-1582 |
| IngestDate | Wed Aug 27 01:08:39 EDT 2025 Tue Sep 09 23:10:31 EDT 2025 Thu Aug 21 06:23:29 EDT 2025 Thu Aug 21 14:12:41 EDT 2025 Fri Sep 05 06:26:47 EDT 2025 Mon Jul 21 06:04:33 EDT 2025 Thu Apr 24 22:54:12 EDT 2025 Tue Jul 01 01:09:43 EDT 2025 Wed Jun 18 06:48:26 EDT 2025 Tue Aug 26 16:33:07 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | This is an open access article under the CC BY license. Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c768t-47b5ebae30d94a2ca3fdec907b8d770eb40a633c2215b183dcac2a73faa5886d3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0001-9561-0239 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1016/j.nicl.2019.101884 |
| PMID | 31200151 |
| PQID | 2242155069 |
| PQPubID | 23479 |
| PageCount | 1 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_1f1b19db179a4f12b2de8cb741848607 swepub_primary_oai_portal_research_lu_se_publications_fd7bfa85_293c_4ca2_a928_5a0d6628e5ec swepub_primary_oai_gup_ub_gu_se_284488 pubmedcentral_primary_oai_pubmedcentral_nih_gov_6562316 proquest_miscellaneous_2242155069 pubmed_primary_31200151 crossref_citationtrail_10_1016_j_nicl_2019_101884 crossref_primary_10_1016_j_nicl_2019_101884 elsevier_clinicalkeyesjournals_1_s2_0_S2213158219302347 elsevier_clinicalkey_doi_10_1016_j_nicl_2019_101884 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2019-01-01 |
| PublicationDateYYYYMMDD | 2019-01-01 |
| PublicationDate_xml | – month: 01 year: 2019 text: 2019-01-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Netherlands |
| PublicationPlace_xml | – name: Netherlands |
| PublicationTitle | NeuroImage clinical |
| PublicationTitleAlternate | Neuroimage Clin |
| PublicationYear | 2019 |
| Publisher | Elsevier Inc Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier |
| References | Ascher, Dubois, Hinsen, Hugunin, Oliphant (bb0005) 1999 Jones, Oliphant, Peterson (bb0085) 2014 Dadar, Maranzano, Misquitta, Anor, Fonov, Tartaglia, Carmichael, Decarli, Collins, Initiative (bb0040) 2017; 157 Zhang, Cloonan, Fitzpatrick, Kanakis, Ayres, Furie, Rosand, Rost (bb0145) 2015; 24 Taha, Hanbury (bb0120) 2015; 15 Avants, Tustison, Song, Cook, Klein, Gee (bb0015) 2011; 54 Schirmer, Giese, Fotiadis, Etherton, Cloonan, Viswanathan, Greenberg, Wu, Rost (bb0095) 2018; 10 Chen, Gurol, Rosand, Viswanathan, Rakich, Groover, Greenberg, Smith (bb0025) 2006; 67 Caligiuri, Perrotta, Augimeri, Rocca, Quattrone, Cherubini (bb0020) 2015; 13 Giese, Schirmer, Donahue, Cloonan, Irie, Winzeck, Bouts, McIntosh, Mocking, Dalca (bb0070) 2017; 3 Van der Walt, Schönberger, Nunez-Iglesias, Boulogne, Warner, Yager, Gouillart, Yu (bb0125) 2014; 2 Dice (bb9029) 1945; 26 Schirmer, Dalca, Sridharan, Giese, Broderick, Jimenez-Conde, Holmegaard, Kissela, Kleindorfer, Lemmens (bb0090) 2017; 88 Dalca, Sridharan, Cloonan, Fitzpatrick, Kanakis, Furie, Rosand, Wu, Sabuncu, Rost (bb0045) 2014 Iglewicz, Hoaglin (bb0080) 1993 Sridharan, Dalca, Golland (bb0115) 2014 Etherton, Wu, Cougo, Giese, Cloonan, Fitzpatrick, Kanakis, Boulouis, Karadeli, Lauer (bb0065) 2017; 88 Zeiler (bb0140) 2012 Cheng (bb0030) 1995; 17 Iglesias, Liu, Thompson, Tu (bb0075) 2011; 30 Smith (bb0100) 2002; 17 Wardlaw, Smith, Biessels, Cordonnier, Fazekas, Frayne, Lindley, T O'Brien, Barkhof, Benavente (bb0135) 2013; 12 Atwood, Wolf, Heard-Costa, Massaro, Beiser, D'Agostino, DeCarli (bb0010) 2004; 35 Sridharan, Dalca, Fitzpatrick, Cloonan, Kanakis, Wu, Furie, Rosand, Rost, Golland (bb0110) 2013 Cloonan, Fitzpatrick, Kanakis, Furie, Rosand, Rost (bb0035) 2015; 240 Dalca, Guttag, Sabuncu (bb0050) 2018 Smith (bb0105) 2010; 41 Debette, Markus (bb0055) 2010; 341 DeCarli, Fletcher, Ramey, Harvey, Jagust (bb0060) 2005; 36 Wack, Dwyer, Bergsland, Di Perri, Ranza, Hussein, Ramasamy, Poloni, Zivadinov (bb0130) 2012; 12 Dice (10.1016/j.nicl.2019.101884_bb9029) 1945; 26 Chen (10.1016/j.nicl.2019.101884_bb0025) 2006; 67 Etherton (10.1016/j.nicl.2019.101884_bb0065) 2017; 88 Schirmer (10.1016/j.nicl.2019.101884_bb0090) 2017; 88 Taha (10.1016/j.nicl.2019.101884_bb0120) 2015; 15 Giese (10.1016/j.nicl.2019.101884_bb0070) 2017; 3 DeCarli (10.1016/j.nicl.2019.101884_bb0060) 2005; 36 Dadar (10.1016/j.nicl.2019.101884_bb0040) 2017; 157 Ascher (10.1016/j.nicl.2019.101884_bb0005) 1999 Iglesias (10.1016/j.nicl.2019.101884_bb0075) 2011; 30 Sridharan (10.1016/j.nicl.2019.101884_bb0110) 2013 Caligiuri (10.1016/j.nicl.2019.101884_bb0020) 2015; 13 Smith (10.1016/j.nicl.2019.101884_bb0105) 2010; 41 Sridharan (10.1016/j.nicl.2019.101884_bb0115) 2014 Avants (10.1016/j.nicl.2019.101884_bb0015) 2011; 54 Wack (10.1016/j.nicl.2019.101884_bb0130) 2012; 12 Cheng (10.1016/j.nicl.2019.101884_bb0030) 1995; 17 Atwood (10.1016/j.nicl.2019.101884_bb0010) 2004; 35 Jones (10.1016/j.nicl.2019.101884_bb0085) 2014 Van der Walt (10.1016/j.nicl.2019.101884_bb0125) 2014; 2 Iglewicz (10.1016/j.nicl.2019.101884_bb0080) 1993 Schirmer (10.1016/j.nicl.2019.101884_bb0095) 2018; 10 Zhang (10.1016/j.nicl.2019.101884_bb0145) 2015; 24 Smith (10.1016/j.nicl.2019.101884_bb0100) 2002; 17 Debette (10.1016/j.nicl.2019.101884_bb0055) 2010; 341 Wardlaw (10.1016/j.nicl.2019.101884_bb0135) 2013; 12 Zeiler (10.1016/j.nicl.2019.101884_bb0140) 2012 Cloonan (10.1016/j.nicl.2019.101884_bb0035) 2015; 240 Dalca (10.1016/j.nicl.2019.101884_bb0045) 2014 Dalca (10.1016/j.nicl.2019.101884_bb0050) 2018 |
| References_xml | – year: 2014 ident: bb0085 article-title: {SciPy}: Open Source Scientific Tools for {Python} – volume: 88 start-page: 1701 year: 2017 end-page: 1708 ident: bb0065 article-title: Integrity of normal-appearing white matter and functional outcomes after acute ischemic stroke publication-title: Neurology – volume: 41 start-page: S139 year: 2010 end-page: S143 ident: bb0105 article-title: Leukoaraiosis and stroke publication-title: Stroke – volume: 17 start-page: 143 year: 2002 end-page: 155 ident: bb0100 article-title: Fast robust automated brain extraction publication-title: Hum. Brain Mapp. – volume: 54 start-page: 2033 year: 2011 end-page: 2044 ident: bb0015 article-title: A reproducible evaluation of ANTs similarity metric performance in brain image registration publication-title: Neuroimage – start-page: 18 year: 2013 end-page: 30 ident: bb0110 article-title: Quantification and analysis of large multimodal clinical image studies: Application to stroke publication-title: International Workshop on Multimodal Brain Image Analysis – volume: 12 start-page: 822 year: 2013 end-page: 838 ident: bb0135 article-title: Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration publication-title: Lancet Neurol. – volume: 35 start-page: 1609 year: 2004 end-page: 1613 ident: bb0010 article-title: Genetic variation in white matter hyperintensity volume in the Framingham Study publication-title: Stroke – start-page: 9290 year: 2018 end-page: 9299 ident: bb0050 article-title: Anatomical priors in convolutional networks for unsupervised biomedical segmentation publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 24 start-page: 649 year: 2015 end-page: 654 ident: bb0145 article-title: Determinants of white matter hyperintensity burden differ at the extremes of ages of ischemic stroke onset publication-title: J. Stroke Cerebrovasc. Dis. – volume: 17 start-page: 790 year: 1995 end-page: 799 ident: bb0030 article-title: Mean shift, mode seeking, and clustering publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – year: 2012 ident: bb0140 article-title: ADADELTA: an adaptive learning rate method – volume: 13 start-page: 261 year: 2015 end-page: 276 ident: bb0020 article-title: Automatic detection of white matter hyperintensities in healthy aging and pathology using magnetic resonance imaging: a review publication-title: Neuroinformatics – volume: 157 start-page: 233 year: 2017 ident: bb0040 article-title: Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging publication-title: NeuroImage – year: 1993 ident: bb0080 article-title: How to Detect and Handle Outliers – volume: 12 start-page: 17 year: 2012 ident: bb0130 article-title: Improved assessment of multiple sclerosis lesion segmentation agreement via detection and outline error estimates publication-title: BMC Med. Imaging – volume: 240 start-page: 149 year: 2015 end-page: 153 ident: bb0035 article-title: Metabolic determinants of white matter hyperintensity burden in patients with ischemic stroke publication-title: Atherosclerosis – volume: 26 start-page: 297 year: 1945 end-page: 302 ident: bb9029 article-title: Measures of the Amount of Ecologic Association Between Species publication-title: Ecology. – volume: 36 start-page: 50 year: 2005 end-page: 55 ident: bb0060 article-title: Anatomical mapping of white matter hyperintensities (WMH): exploring the relationships between periventricular WMH, deep WMH, and total WMH burden publication-title: Stroke – volume: 10 start-page: 208 year: 2018 ident: bb0095 article-title: Spatial signature of white matter hyperintensities in stroke patients publication-title: BioRxiv – year: 1999 ident: bb0005 article-title: Numerical Python, UCRL-MA-128569 – volume: 3 start-page: e180 year: 2017 ident: bb0070 article-title: Design and rationale for examining neuroimaging genetics in ischemic stroke: the MRI-GENIE study publication-title: Neurol. Genet. – volume: 2 start-page: e453 year: 2014 ident: bb0125 article-title: Scikit-image: image processing in Python publication-title: PeerJ – year: 2014 ident: bb0115 article-title: An interactive visualization tool for nipype medical image computing pipelines publication-title: MICCAI-IMIC Interactive Medical Image Computing Workshop – volume: 88 year: 2017 ident: bb0090 article-title: Fully automated pipeline for white matter hyperintensity segmentation using clinical scans in patients with acute ischemic stroke (S19. 001) publication-title: Neurology – volume: 341 start-page: c3666 year: 2010 ident: bb0055 article-title: The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis publication-title: Bmj – volume: 67 start-page: 83 year: 2006 end-page: 87 ident: bb0025 article-title: Progression of white matter lesions and hemorrhages in cerebral amyloid angiopathy publication-title: Neurology – start-page: 773 year: 2014 ident: bb0045 article-title: Segmentation of cerebrovascular pathologies in stroke patients with spatial and shape priors publication-title: Medical Image Computing and Computer-Assisted Intervention: MICCAI. International Conference on Medical Image Computing and Computer-Assisted Intervention – volume: 30 start-page: 1617 year: 2011 end-page: 1634 ident: bb0075 article-title: Robust brain extraction across datasets and comparison with publicly available methods publication-title: IEEE Trans. Med. Imaging – volume: 15 start-page: 29 year: 2015 ident: bb0120 article-title: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool publication-title: BMC Med. Imaging – start-page: 18 year: 2013 ident: 10.1016/j.nicl.2019.101884_bb0110 article-title: Quantification and analysis of large multimodal clinical image studies: Application to stroke – volume: 3 start-page: e180 year: 2017 ident: 10.1016/j.nicl.2019.101884_bb0070 article-title: Design and rationale for examining neuroimaging genetics in ischemic stroke: the MRI-GENIE study publication-title: Neurol. Genet. doi: 10.1212/NXG.0000000000000180 – volume: 10 start-page: 208 year: 2018 ident: 10.1016/j.nicl.2019.101884_bb0095 article-title: Spatial signature of white matter hyperintensities in stroke patients publication-title: BioRxiv – volume: 12 start-page: 822 year: 2013 ident: 10.1016/j.nicl.2019.101884_bb0135 article-title: Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration publication-title: Lancet Neurol. doi: 10.1016/S1474-4422(13)70124-8 – volume: 157 start-page: 233 year: 2017 ident: 10.1016/j.nicl.2019.101884_bb0040 article-title: Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging publication-title: NeuroImage doi: 10.1016/j.neuroimage.2017.06.009 – volume: 67 start-page: 83 year: 2006 ident: 10.1016/j.nicl.2019.101884_bb0025 article-title: Progression of white matter lesions and hemorrhages in cerebral amyloid angiopathy publication-title: Neurology doi: 10.1212/01.wnl.0000223613.57229.24 – volume: 88 issue: S19 year: 2017 ident: 10.1016/j.nicl.2019.101884_bb0090 article-title: Fully automated pipeline for white matter hyperintensity segmentation using clinical scans in patients with acute ischemic stroke (S19. 001) publication-title: Neurology – volume: 24 start-page: 649 year: 2015 ident: 10.1016/j.nicl.2019.101884_bb0145 article-title: Determinants of white matter hyperintensity burden differ at the extremes of ages of ischemic stroke onset publication-title: J. Stroke Cerebrovasc. Dis. doi: 10.1016/j.jstrokecerebrovasdis.2014.10.016 – volume: 30 start-page: 1617 year: 2011 ident: 10.1016/j.nicl.2019.101884_bb0075 article-title: Robust brain extraction across datasets and comparison with publicly available methods publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2011.2138152 – volume: 341 start-page: c3666 year: 2010 ident: 10.1016/j.nicl.2019.101884_bb0055 article-title: The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis publication-title: Bmj doi: 10.1136/bmj.c3666 – start-page: 773 year: 2014 ident: 10.1016/j.nicl.2019.101884_bb0045 article-title: Segmentation of cerebrovascular pathologies in stroke patients with spatial and shape priors – volume: 17 start-page: 790 year: 1995 ident: 10.1016/j.nicl.2019.101884_bb0030 article-title: Mean shift, mode seeking, and clustering publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.400568 – volume: 36 start-page: 50 year: 2005 ident: 10.1016/j.nicl.2019.101884_bb0060 article-title: Anatomical mapping of white matter hyperintensities (WMH): exploring the relationships between periventricular WMH, deep WMH, and total WMH burden publication-title: Stroke doi: 10.1161/01.STR.0000150668.58689.f2 – year: 1993 ident: 10.1016/j.nicl.2019.101884_bb0080 – volume: 17 start-page: 143 year: 2002 ident: 10.1016/j.nicl.2019.101884_bb0100 article-title: Fast robust automated brain extraction publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.10062 – volume: 26 start-page: 297 issue: 3 year: 1945 ident: 10.1016/j.nicl.2019.101884_bb9029 article-title: Measures of the Amount of Ecologic Association Between Species publication-title: Ecology. doi: 10.2307/1932409 – volume: 88 start-page: 1701 year: 2017 ident: 10.1016/j.nicl.2019.101884_bb0065 article-title: Integrity of normal-appearing white matter and functional outcomes after acute ischemic stroke publication-title: Neurology doi: 10.1212/WNL.0000000000003890 – volume: 41 start-page: S139 year: 2010 ident: 10.1016/j.nicl.2019.101884_bb0105 article-title: Leukoaraiosis and stroke publication-title: Stroke doi: 10.1161/STROKEAHA.110.596056 – year: 1999 ident: 10.1016/j.nicl.2019.101884_bb0005 – year: 2012 ident: 10.1016/j.nicl.2019.101884_bb0140 – year: 2014 ident: 10.1016/j.nicl.2019.101884_bb0115 article-title: An interactive visualization tool for nipype medical image computing pipelines – start-page: 9290 year: 2018 ident: 10.1016/j.nicl.2019.101884_bb0050 article-title: Anatomical priors in convolutional networks for unsupervised biomedical segmentation – volume: 35 start-page: 1609 year: 2004 ident: 10.1016/j.nicl.2019.101884_bb0010 article-title: Genetic variation in white matter hyperintensity volume in the Framingham Study publication-title: Stroke doi: 10.1161/01.STR.0000129643.77045.10 – volume: 54 start-page: 2033 year: 2011 ident: 10.1016/j.nicl.2019.101884_bb0015 article-title: A reproducible evaluation of ANTs similarity metric performance in brain image registration publication-title: Neuroimage doi: 10.1016/j.neuroimage.2010.09.025 – volume: 240 start-page: 149 year: 2015 ident: 10.1016/j.nicl.2019.101884_bb0035 article-title: Metabolic determinants of white matter hyperintensity burden in patients with ischemic stroke publication-title: Atherosclerosis doi: 10.1016/j.atherosclerosis.2015.02.052 – year: 2014 ident: 10.1016/j.nicl.2019.101884_bb0085 – volume: 12 start-page: 17 year: 2012 ident: 10.1016/j.nicl.2019.101884_bb0130 article-title: Improved assessment of multiple sclerosis lesion segmentation agreement via detection and outline error estimates publication-title: BMC Med. Imaging doi: 10.1186/1471-2342-12-17 – volume: 2 start-page: e453 year: 2014 ident: 10.1016/j.nicl.2019.101884_bb0125 article-title: Scikit-image: image processing in Python publication-title: PeerJ doi: 10.7717/peerj.453 – volume: 13 start-page: 261 year: 2015 ident: 10.1016/j.nicl.2019.101884_bb0020 article-title: Automatic detection of white matter hyperintensities in healthy aging and pathology using magnetic resonance imaging: a review publication-title: Neuroinformatics doi: 10.1007/s12021-015-9260-y – volume: 15 start-page: 29 year: 2015 ident: 10.1016/j.nicl.2019.101884_bb0120 article-title: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool publication-title: BMC Med. Imaging doi: 10.1186/s12880-015-0068-x |
| SSID | ssj0000800766 |
| Score | 2.3829498 |
| Snippet | White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such... AbstractWhite matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of... |
| SourceID | doaj swepub pubmedcentral proquest pubmed crossref elsevier |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 101884 |
| SubjectTerms | Adult Aged Aged, 80 and over Brain Ischemia - diagnostic imaging burden Clinical Medicine Cohort Studies determinants Engineering and Technology Female Humans Klinisk medicin Magnetic Resonance Imaging - methods Male Medical and Health Sciences Medical Engineering Medical Imaging Medicin och hälsovetenskap Medicinsk bildvetenskap Medicinteknik Middle Aged Neuroimaging - methods Neurologi Neurology Neurosciences Neurosciences & Neurology Neurovetenskaper Radiologi och bildbehandling Radiology Radiology and Medical Imaging Regular Stroke - diagnostic imaging Teknik White Matter - diagnostic imaging |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQD4gL4s3ykpEQFxQRO07iHAFtaZG2B6BSxWXkV7rbluzSbCR64z_wD_kljO1ktVGrcuHq-CF7vtgznvE3hLzKHeNWGJmo0rsZnSoSbWyZCLQlVI4WtAtvq2YHxd6h-HSUH22l-vIxYZEeOC7cW1YzzSqrEThK1Ixrbp002pOu-PxJ4R15WqVbxtRJrweVwVHJOcsSlkvev5iJwV2eddbHdVW-QEoxOpUCef_ocLqsfF6OoRwxjYbTafcOud2rlfRdnM5dcsM198jNWe84v09-hkR49Htg06TzC89vHIPX1xf0R6diyFCQEl009MzHhyctys_R4e0kVabDLhZoDvuAetquz5en-Hk5904H-ufXb4qYo7PP-8nH6cH-lAbm2gfkcHf69cNe0iddSAxaHutElDp3WrkstZVQ3Kists6gCa2lLcvUaZGqIssMrmuucT-wRhmOgq6VyqUsbPaQ7DTLxj0m1MlUOMu00loKVMxU5YTj1mIPsrRaTAgbFh1Mz0juE2OcwRB6dgJeUOAFBVFQE_Jm02YV-Tiurf3ey3JT03NphwJEGPQIg38hbEKyAQkwLDlusNjR4tqhy6taubbfI1pg0HJI4YtHqAcoqtKoQAkc7-UAN8B_3TtwVOOWXQvc--_RpCyqCXkU4beZWsZ8dFzOcNwRMEdzH39pFvPAJ154HZgVE_I6QnjU5LhbARYdd9A6QF0GN_wJ-XZFxWguQs9RNYez0GK1dfkMtS11rWQOqGMaEEZxUBWXkKvUFgWXLnfmyf8Q2VNyy4siXpU9Izvr8849R-VxrV-EfeIv9pRwUg priority: 102 providerName: Directory of Open Access Journals |
| Title | White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts – The MRI-GENIE study |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S2213158219302347 https://www.clinicalkey.es/playcontent/1-s2.0-S2213158219302347 https://www.ncbi.nlm.nih.gov/pubmed/31200151 https://www.proquest.com/docview/2242155069 https://pubmed.ncbi.nlm.nih.gov/PMC6562316 https://gup.ub.gu.se/publication/284488 https://doaj.org/article/1f1b19db179a4f12b2de8cb741848607 |
| Volume | 23 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3db9MwELfGkKa9IL4XPiYjIV5QUOM4ifOAEKCODal7ACpNvFj-SttR0q5pEP3vuXOSQrSyV3_Kd2f7znf-HSEvExcxy40IVYZuRqfSUBubhRxsCZWABe3836rReXo65p8vkos90qU7aglY7TTtMJ_UeDV_8_tq8w42_Nu_sVoIIothWjkWCMFvkdveX4ShfK26f9lqR1matn9ndnc9JAdxhIFGSdS7qjyif-_Guq6RXg-s7MGP-ivr5C650-qa9H0jHPfInivvk4NR601_QH757Hj0p4fYpNMNgh43Ee3rDb2qVRNH5FlHZyWdY9B4WAFTHe0-VFJlahhiBoTEKHtarVeLH1C9mKIngoYUxJCOvpyFn4bnZ0PqwWwfkvHJ8NvH07DNwxAaMEbWIc904rRy8cDmXDGj4sI6A1a1FjbLBk7zgUrj2DBQHzQcEdYow4D3hVKJEKmNH5H9clG6I0KdGHBnI620Fhx0NZU77pi1MILIrOYBiTqSS9OClGOujLnsotEuJXJMIsdkw7GAvN72WTYQHTe2_oCc3LZEeG1fsFhNZLtbZVREOsqthtNK8SJimlknjEakH0zalQUk7uRAdgSHMxcGmt04dbarl6s6qZeRrJgcyK9AyTjCL8w55nTiMN-LTtgkbH_06ajSLepKMnTpg5WZ5gF53AjfdmmdHMO8PbHsrb1fU86mHmI8RbU4SgPyqhHgXpdJvZRQNKll5SSoN3AHBOT7jobN5pUtbNVUzn2P5T_v0bKwmS6USCSonUZyo5hUORMyUQObpky4xJkn_13ZU3KI9G2exJ6R_fWqds9BSVzrY_-4cuz3_x9H6WkV |
| linkProvider | Scholars Portal |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=White+matter+hyperintensity+quantification+in+large-scale+clinical+acute+ischemic+stroke+cohorts+-+The+MRI-GENIE+study&rft.jtitle=NeuroImage+clinical&rft.au=Schirmer%2C+Markus+D&rft.au=Dalca%2C+Adrian+V&rft.au=Sridharan%2C+Ramesh&rft.au=Giese%2C+Anne-Katrin&rft.date=2019-01-01&rft.eissn=2213-1582&rft.volume=23&rft.spage=101884&rft_id=info:doi/10.1016%2Fj.nicl.2019.101884&rft_id=info%3Apmid%2F31200151&rft.externalDocID=31200151 |
| thumbnail_m | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fcdn.clinicalkey.com%2Fck-thumbnails%2F22131582%2FS2213158219X00030%2Fcov150h.gif |