Accuracy and reproducibility of automated white matter hyperintensities segmentation with lesion segmentation tool: A European multi-site 3T study

Brain vascular damage accumulate in aging and often manifest as white matter hyperintensities (WMHs) on MRI. Despite increased interest in automated methods to segment WMHs, a gold standard has not been achieved and their longitudinal reproducibility has been poorly investigated. The aim of present...

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Vydané v:	Magnetic resonance imaging Ročník 76; s. 108 - 115
Hlavní autori:	Ribaldi, Federica, Altomare, Daniele, Jovicich, Jorge, Ferrari, Clarissa, Picco, Agnese, Pizzini, Francesca Benedetta, Soricelli, Andrea, Mega, Anna, Ferretti, Antonio, Drevelegas, Antonios, Bosch, Beatriz, Müller, Bernhard W., Marra, Camillo, Cavaliere, Carlo, Bartrés-Faz, David, Nobili, Flavio, Alessandrini, Franco, Barkhof, Frederik, Gros-Dagnac, Helene, Ranjeva, Jean-Philippe, Wiltfang, Jens, Kuijer, Joost, Sein, Julien, Hoffmann, Karl-Titus, Roccatagliata, Luca, Parnetti, Lucilla, Tsolaki, Magda, Constantinidis, Manos, Aiello, Marco, Salvatore, Marco, Montalti, Martina, Caulo, Massimo, Didic, Mira, Bargallo, Núria, Blin, Olivier, Rossini, Paolo M, Schonknecht, Peter, Floridi, Piero, Payoux, Pierre, Visser, Pieter Jelle, Bordet, Régis, Lopes, Renaud, Tarducci, Roberto, Bombois, Stephanie, Hensch, Tilman, Fiedler, Ute, Richardson, Jill C., Frisoni, Giovanni B., Marizzoni, Moira
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier Inc 01.02.2021 Elsevier
Predmet:	Accuracy Adult Aging Algorithms Automated segmentation algorithms Automation Bioengineering Cross-Sectional Studies Female Humans Image Processing, Computer-Assisted - methods Imaging Lesion segmentation toolbox Life Sciences Magnetic Resonance Imaging Male Reproducibility Reproducibility of Results White Matter - diagnostic imaging White Matter - pathology White matter hyperintensities White matter hyperintensities Accuracy Automated segmentation algorithms Lesion segmentation toolbox Reproducibility
ISSN:	0730-725X, 1873-5894, 1873-5894
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Abstract	Brain vascular damage accumulate in aging and often manifest as white matter hyperintensities (WMHs) on MRI. Despite increased interest in automated methods to segment WMHs, a gold standard has not been achieved and their longitudinal reproducibility has been poorly investigated. The aim of present work is to evaluate accuracy and reproducibility of two freely available segmentation algorithms. A harmonized MRI protocol was implemented in 3T-scanners across 13 European sites, each scanning five volunteers twice (test-retest) using 2D-FLAIR. Automated segmentation was performed using Lesion segmentation tool algorithms (LST): the Lesion growth algorithm (LGA) in SPM8 and 12 and the Lesion prediction algorithm (LPA). To assess reproducibility, we applied the LST longitudinal pipeline to the LGA and LPA outputs for both the test and retest scans. We evaluated volumetric and spatial accuracy comparing LGA and LPA with manual tracing, and for reproducibility the test versus retest. Median volume difference between automated WMH and manual segmentations (mL) was −0.22[IQR = 0.50] for LGA-SPM8, −0.12[0.57] for LGA-SPM12, −0.09[0.53] for LPA, while the spatial accuracy (Dice Coefficient) was 0.29[0.31], 0.33[0.26] and 0.41[0.23], respectively. The reproducibility analysis showed a median reproducibility error of 20%[IQR = 41] for LGA-SPM8, 14% [31] for LGA-SPM12 and 10% [27] with the LPA cross-sectional pipeline. Applying the LST longitudinal pipeline, the reproducibility errors were considerably reduced (LGA: 0%[IQR = 0], p < 0.001; LPA: 0% [3], p < 0.001) compared to those derived using the cross-sectional algorithms. The DC using the longitudinal pipeline was excellent (median = 1) for LGA [IQR = 0] and LPA [0.02]. LST algorithms showed moderate accuracy and good reproducibility. Therefore, it can be used as a reliable cross-sectional and longitudinal tool in multi-site studies. [Display omitted] •A reliable quantification of white matter hyperintensities using lesion prediction algorithm (SPM12 LST) is possible.•The longitudinal pipeline of lesion segmentation toolbox can accurately assess white matter changes over time.•Lesion prediction algorithm output is not affected by site or scanner effects.
AbstractList	Brain vascular damage accumulate in aging and often manifest as white matter hyperintensities (WMHs) on MRI. Despite increased interest in automated methods to segment WMHs, a gold standard has not been achieved and their longitudinal reproducibility has been poorly investigated. The aim of present work is to evaluate accuracy and reproducibility of two freely available segmentation algorithms. A harmonized MRI protocol was implemented in 3T-scanners across 13 European sites, each scanning five volunteers twice (test-retest) using 2D-FLAIR. Automated segmentation was performed using Lesion segmentation tool algorithms (LST): the Lesion growth algorithm (LGA) in SPM8 and 12 and the Lesion prediction algorithm (LPA). To assess reproducibility, we applied the LST longitudinal pipeline to the LGA and LPA outputs for both the test and retest scans. We evaluated volumetric and spatial accuracy comparing LGA and LPA with manual tracing, and for reproducibility the test versus retest. Median volume difference between automated WMH and manual segmentations (mL) was −0.22[IQR = 0.50] for LGA-SPM8, −0.12[0.57] for LGA-SPM12, −0.09[0.53] for LPA, while the spatial accuracy (Dice Coefficient) was 0.29[0.31], 0.33[0.26] and 0.41[0.23], respectively. The reproducibility analysis showed a median reproducibility error of 20%[IQR = 41] for LGA-SPM8, 14% [31] for LGA-SPM12 and 10% [27] with the LPA cross-sectional pipeline. Applying the LST longitudinal pipeline, the reproducibility errors were considerably reduced (LGA: 0%[IQR = 0], p < 0.001; LPA: 0% [3], p < 0.001) compared to those derived using the cross-sectional algorithms. The DC using the longitudinal pipeline was excellent (median = 1) for LGA [IQR = 0] and LPA [0.02]. LST algorithms showed moderate accuracy and good reproducibility. Therefore, it can be used as a reliable cross-sectional and longitudinal tool in multi-site studies. [Display omitted] •A reliable quantification of white matter hyperintensities using lesion prediction algorithm (SPM12 LST) is possible.•The longitudinal pipeline of lesion segmentation toolbox can accurately assess white matter changes over time.•Lesion prediction algorithm output is not affected by site or scanner effects. Brain vascular damage accumulate in aging and often manifest as white matter hyperintensities (WMHs) on MRI. Despite increased interest in automated methods to segment WMHs, a gold standard has not been achieved and their longitudinal reproducibility has been poorly investigated. The aim of present work is to evaluate accuracy and reproducibility of two freely available segmentation algorithms. A harmonized MRI protocol was implemented in 3T-scanners across 13 European sites, each scanning five volunteers twice (test-retest) using 2D-FLAIR. Automated segmentation was performed using Lesion segmentation tool algorithms (LST): the Lesion growth algorithm (LGA) in SPM8 and 12 and the Lesion prediction algorithm (LPA). To assess reproducibility, we applied the LST longitudinal pipeline to the LGA and LPA outputs for both the test and retest scans. We evaluated volumetric and spatial accuracy comparing LGA and LPA with manual tracing, and for reproducibility the test versus retest. Median volume difference between automated WMH and manual segmentations (mL) was -0.22[IQR = 0.50] for LGA-SPM8, -0.12[0.57] for LGA-SPM12, -0.09[0.53] for LPA, while the spatial accuracy (Dice Coefficient) was 0.29[0.31], 0.33[0.26] and 0.41[0.23], respectively. The reproducibility analysis showed a median reproducibility error of 20%[IQR = 41] for LGA-SPM8, 14% [31] for LGA-SPM12 and 10% [27] with the LPA cross-sectional pipeline. Applying the LST longitudinal pipeline, the reproducibility errors were considerably reduced (LGA: 0%[IQR = 0], p < 0.001; LPA: 0% [3], p < 0.001) compared to those derived using the cross-sectional algorithms. The DC using the longitudinal pipeline was excellent (median = 1) for LGA [IQR = 0] and LPA [0.02]. LST algorithms showed moderate accuracy and good reproducibility. Therefore, it can be used as a reliable cross-sectional and longitudinal tool in multi-site studies. Brain vascular damage accumulate in aging and often manifest as white matter hyperintensities (WMHs) on MRI. Despite increased interest in automated methods to segment WMHs, a gold standard has not been achieved and their longitudinal reproducibility has been poorly investigated. The aim of present work is to evaluate accuracy and reproducibility of two freely available segmentation algorithms. A harmonized MRI protocol was implemented in 3T-scanners across 13 European sites, each scanning five volunteers twice (test-retest) using 2D-FLAIR. Automated segmentation was performed using Lesion segmentation tool algorithms (LST): the Lesion growth algorithm (LGA) in SPM8 and 12 and the Lesion prediction algorithm (LPA). To assess reproducibility, we applied the LST longitudinal pipeline to the LGA and LPA outputs for both the test and retest scans. We evaluated volumetric and spatial accuracy comparing LGA and LPA with manual tracing, and for reproducibility the test versus retest. Median volume difference between automated WMH and manual segmentations (mL) was -0.22[IQR = 0.50] for LGA-SPM8, -0.12[0.57] for LGA-SPM12, -0.09[0.53] for LPA, while the spatial accuracy (Dice Coefficient) was 0.29[0.31], 0.33[0.26] and 0.41[0.23], respectively. The reproducibility analysis showed a median reproducibility error of 20%[IQR = 41] for LGA-SPM8, 14% [31] for LGA-SPM12 and 10% [27] with the LPA cross-sectional pipeline. Applying the LST longitudinal pipeline, the reproducibility errors were considerably reduced (LGA: 0%[IQR = 0], p < 0.001; LPA: 0% [3], p < 0.001) compared to those derived using the cross-sectional algorithms. The DC using the longitudinal pipeline was excellent (median = 1) for LGA [IQR = 0] and LPA [0.02]. LST algorithms showed moderate accuracy and good reproducibility. Therefore, it can be used as a reliable cross-sectional and longitudinal tool in multi-site studies. Brain vascular damage accumulate in aging and often manifest as white matter hyperintensities (WMHs) on MRI. Despite increased interest in automated methods to segment WMHs, a gold standard has not been achieved and their longitudinal reproducibility has been poorly investigated. The aim of present work is to evaluate accuracy and reproducibility of two freely available segmentation algorithms. A harmonized MRI protocol was implemented in 3T-scanners across 13 European sites, each scanning five volunteers twice (test-retest) using 2D-FLAIR. Automated segmentation was performed using Lesion segmentation tool algorithms (LST): the Lesion growth algorithm (LGA) in SPM8 and 12 and the Lesion prediction algorithm (LPA). To assess reproducibility, we applied the LST longitudinal pipeline to the LGA and LPA outputs for both the test and retest scans. We evaluated volumetric and spatial accuracy comparing LGA and LPA with manual tracing, and for reproducibility the test versus retest. Median volume difference between automated WMH and manual segmentations (mL) was -0.22[IQR = 0.50] for LGA-SPM8, -0.12[0.57] for LGA-SPM12, -0.09[0.53] for LPA, while the spatial accuracy (Dice Coefficient) was 0.29[0.31], 0.33[0.26] and 0.41[0.23], respectively. The reproducibility analysis showed a median reproducibility error of 20%[IQR = 41] for LGA-SPM8, 14% [31] for LGA-SPM12 and 10% [27] with the LPA cross-sectional pipeline. Applying the LST longitudinal pipeline, the reproducibility errors were considerably reduced (LGA: 0%[IQR = 0], p < 0.001; LPA: 0% [3], p < 0.001) compared to those derived using the cross-sectional algorithms. The DC using the longitudinal pipeline was excellent (median = 1) for LGA [IQR = 0] and LPA [0.02]. LST algorithms showed moderate accuracy and good reproducibility. Therefore, it can be used as a reliable cross-sectional and longitudinal tool in multi-site studies.Brain vascular damage accumulate in aging and often manifest as white matter hyperintensities (WMHs) on MRI. Despite increased interest in automated methods to segment WMHs, a gold standard has not been achieved and their longitudinal reproducibility has been poorly investigated. The aim of present work is to evaluate accuracy and reproducibility of two freely available segmentation algorithms. A harmonized MRI protocol was implemented in 3T-scanners across 13 European sites, each scanning five volunteers twice (test-retest) using 2D-FLAIR. Automated segmentation was performed using Lesion segmentation tool algorithms (LST): the Lesion growth algorithm (LGA) in SPM8 and 12 and the Lesion prediction algorithm (LPA). To assess reproducibility, we applied the LST longitudinal pipeline to the LGA and LPA outputs for both the test and retest scans. We evaluated volumetric and spatial accuracy comparing LGA and LPA with manual tracing, and for reproducibility the test versus retest. Median volume difference between automated WMH and manual segmentations (mL) was -0.22[IQR = 0.50] for LGA-SPM8, -0.12[0.57] for LGA-SPM12, -0.09[0.53] for LPA, while the spatial accuracy (Dice Coefficient) was 0.29[0.31], 0.33[0.26] and 0.41[0.23], respectively. The reproducibility analysis showed a median reproducibility error of 20%[IQR = 41] for LGA-SPM8, 14% [31] for LGA-SPM12 and 10% [27] with the LPA cross-sectional pipeline. Applying the LST longitudinal pipeline, the reproducibility errors were considerably reduced (LGA: 0%[IQR = 0], p < 0.001; LPA: 0% [3], p < 0.001) compared to those derived using the cross-sectional algorithms. The DC using the longitudinal pipeline was excellent (median = 1) for LGA [IQR = 0] and LPA [0.02]. LST algorithms showed moderate accuracy and good reproducibility. Therefore, it can be used as a reliable cross-sectional and longitudinal tool in multi-site studies.
Author	Marizzoni, Moira Müller, Bernhard W. Cavaliere, Carlo Floridi, Piero Nobili, Flavio Ranjeva, Jean-Philippe Jovicich, Jorge Barkhof, Frederik Didic, Mira Tarducci, Roberto Frisoni, Giovanni B. Constantinidis, Manos Mega, Anna Ferretti, Antonio Soricelli, Andrea Visser, Pieter Jelle Aiello, Marco Fiedler, Ute Parnetti, Lucilla Ribaldi, Federica Bartrés-Faz, David Lopes, Renaud Wiltfang, Jens Richardson, Jill C. Picco, Agnese Alessandrini, Franco Bosch, Beatriz Drevelegas, Antonios Bargallo, Núria Schonknecht, Peter Marra, Camillo Payoux, Pierre Rossini, Paolo M Hensch, Tilman Tsolaki, Magda Blin, Olivier Bordet, Régis Sein, Julien Altomare, Daniele Caulo, Massimo Salvatore, Marco Montalti, Martina Gros-Dagnac, Helene Roccatagliata, Luca Hoffmann, Karl-Titus Pizzini, Francesca Benedetta Bombois, Stephanie Kuijer, Joost Ferrari, Clarissa
Author_xml	– sequence: 1 givenname: Federica surname: Ribaldi fullname: Ribaldi, Federica email: fribaldi@fatebenefratelli.eu organization: Laboratory of Alzheimer's Neuroimaging and Alzheimer's Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy – sequence: 2 givenname: Daniele surname: Altomare fullname: Altomare, Daniele organization: Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland – sequence: 3 givenname: Jorge surname: Jovicich fullname: Jovicich, Jorge organization: Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Italy – sequence: 4 givenname: Clarissa surname: Ferrari fullname: Ferrari, Clarissa organization: Unit of Statistics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy – sequence: 5 givenname: Agnese surname: Picco fullname: Picco, Agnese organization: Department of Neuroscience, Ophthalmology, Genetics and Mother-Child Health (DINOGMI), University of Genoa, Genoa, Italy – sequence: 6 givenname: Francesca Benedetta surname: Pizzini fullname: Pizzini, Francesca Benedetta organization: Radiology, Dept. of Diagnostic and Public Health, Verona University, Verona, Italy – sequence: 7 givenname: Andrea surname: Soricelli fullname: Soricelli, Andrea organization: IRCCS SDN, Naples, Italy – sequence: 8 givenname: Anna surname: Mega fullname: Mega, Anna organization: Laboratory of Alzheimer's Neuroimaging and Alzheimer's Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy – sequence: 9 givenname: Antonio surname: Ferretti fullname: Ferretti, Antonio organization: Department of Neuroscience Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti, Italy – sequence: 10 givenname: Antonios surname: Drevelegas fullname: Drevelegas, Antonios organization: Interbalkan Medical Center of Thessaloniki, Thessaloniki, Greece – sequence: 11 givenname: Beatriz surname: Bosch fullname: Bosch, Beatriz organization: Department of Psychiatry and Clinical Psychobiology, Universitat de Barcelona and IDIBAPS, Barcelona, Spain – sequence: 12 givenname: Bernhard W. surname: Müller fullname: Müller, Bernhard W. organization: LVR-Clinic for Psychiatry and Psychotherapy, Institutes and Clinics of the University Duisburg-Essen, Essen, Germany – sequence: 13 givenname: Camillo surname: Marra fullname: Marra, Camillo organization: Center for Neuropsychological Research, Catholic University, Rome, Italy – sequence: 14 givenname: Carlo surname: Cavaliere fullname: Cavaliere, Carlo organization: IRCCS SDN, Naples, Italy – sequence: 15 givenname: David surname: Bartrés-Faz fullname: Bartrés-Faz, David organization: Department of Psychiatry and Clinical Psychobiology, Universitat de Barcelona and IDIBAPS, Barcelona, Spain – sequence: 16 givenname: Flavio surname: Nobili fullname: Nobili, Flavio organization: Dept. of Neuroscience (DINOGMI), University of Genoa, Italy – sequence: 17 givenname: 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22 givenname: Joost surname: Kuijer fullname: Kuijer, Joost organization: Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, the Netherlands – sequence: 23 givenname: Julien surname: Sein fullname: Sein, Julien organization: Institut de Neurosciences de la Timone (INT), Aix-Marseille Université, CNRS, UMR 7289, 13005 Marseille, France – sequence: 24 givenname: Karl-Titus surname: Hoffmann fullname: Hoffmann, Karl-Titus organization: Department of Neuroradiology, University Hospital Leipzig, Leipzig, Germany – sequence: 25 givenname: Luca surname: Roccatagliata fullname: Roccatagliata, Luca organization: IRCCS Ospedale Policlinico San Martino Genova, Italy – sequence: 26 givenname: Lucilla surname: Parnetti fullname: Parnetti, Lucilla organization: Section of Neurology, Centre for Memory Disturbances, University of Perugia, Perugia, Italy – sequence: 27 givenname: Magda surname: Tsolaki fullname: Tsolaki, Magda organization: 1st Department of Neurology, Aristotle University of Thessaloniki, Makedonia, Greece – sequence: 28 givenname: Manos surname: Constantinidis fullname: Constantinidis, Manos organization: Interbalkan Medical Center of Thessaloniki, Thessaloniki, Greece – sequence: 29 givenname: Marco surname: Aiello fullname: Aiello, Marco organization: IRCCS SDN, Naples, Italy – sequence: 30 givenname: Marco surname: Salvatore fullname: Salvatore, Marco organization: IRCCS SDN, Naples, Italy – sequence: 31 givenname: Martina surname: Montalti fullname: Montalti, Martina organization: Laboratory of Alzheimer's Neuroimaging and Alzheimer's Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy – sequence: 32 givenname: Massimo surname: Caulo fullname: Caulo, Massimo organization: Department of Neuroscience Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti, Italy – sequence: 33 givenname: Mira surname: Didic fullname: Didic, Mira organization: APHM, Timone, Service de Neurologie et Neuropsychologie, APHM Hôpital Timone Adultes, Marseille, France – sequence: 34 givenname: Núria surname: Bargallo fullname: Bargallo, Núria organization: Department of Neuroradiology and Magnetic Resonance Image Core Facility, Hospital Clínic de Barcelona, IDIBAPS, Barcelona, Spain – sequence: 35 givenname: Olivier surname: Blin fullname: Blin, Olivier organization: Aix Marseille University, UMR-INSERM 1106, Service de Pharmacologie Clinique, AP-HM, Marseille, France – sequence: 36 givenname: Paolo M surname: Rossini fullname: Rossini, Paolo M organization: Dept. Neuroscience & Neurorehabilitation, IRCCS-San Raffaele-Pisana, Rome, Italy – sequence: 37 givenname: Peter surname: Schonknecht fullname: Schonknecht, Peter organization: Department of Psychiatry and Psychotherapy, University of Leipzig Medical Center, Leipzig, Germany – sequence: 38 givenname: Piero surname: Floridi fullname: Floridi, Piero organization: Neuroradiology Unit, Perugia General Hospital, Perugia, Italy – sequence: 39 givenname: Pierre surname: Payoux fullname: Payoux, Pierre organization: ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France – sequence: 40 givenname: Pieter Jelle surname: Visser fullname: Visser, Pieter Jelle organization: Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands – sequence: 41 givenname: Régis surname: Bordet fullname: Bordet, Régis organization: Univ. Lille, INSERM, CHU Lille, Lille Neuroscience & Cognition - Degenerative and Vascular Cognitive Disorders-U1172. F-59000 Lille, France – sequence: 42 givenname: Renaud surname: Lopes fullname: Lopes, Renaud organization: Univ. Lille, INSERM, CHU Lille, Lille Neuroscience & Cognition - Degenerative and Vascular Cognitive Disorders-U1172. F-59000 Lille, France – sequence: 43 givenname: Roberto surname: Tarducci fullname: Tarducci, Roberto organization: Medical Physics Unit, Perugia General Hospital, Perugia, Italy – sequence: 44 givenname: Stephanie surname: Bombois fullname: Bombois, Stephanie organization: Univ. Lille, INSERM, CHU Lille, Lille Neuroscience & Cognition - Degenerative and Vascular Cognitive Disorders-U1172. F-59000 Lille, France – sequence: 45 givenname: Tilman surname: Hensch fullname: Hensch, Tilman organization: Department of Psychiatry and Psychotherapy, University of Leipzig Medical Center, Leipzig, Germany – sequence: 46 givenname: Ute surname: Fiedler fullname: Fiedler, Ute organization: LVR-Clinic for Psychiatry and Psychotherapy, Institutes and Clinics of the University Duisburg-Essen, Essen, Germany – sequence: 47 givenname: Jill C. surname: Richardson fullname: Richardson, Jill C. organization: Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage, United Kingdom – sequence: 48 givenname: Giovanni B. surname: Frisoni fullname: Frisoni, Giovanni B. organization: Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland – sequence: 49 givenname: Moira surname: Marizzoni fullname: Marizzoni, Moira organization: Laboratory of Alzheimer's Neuroimaging and Alzheimer's Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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Keywords	White matter hyperintensities Accuracy Automated segmentation algorithms Lesion segmentation toolbox Reproducibility
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Snippet	Brain vascular damage accumulate in aging and often manifest as white matter hyperintensities (WMHs) on MRI. Despite increased interest in automated methods to...
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SubjectTerms	Accuracy Adult Aging Algorithms Automated segmentation algorithms Automation Bioengineering Cross-Sectional Studies Female Humans Image Processing, Computer-Assisted - methods Imaging Lesion segmentation toolbox Life Sciences Magnetic Resonance Imaging Male Reproducibility Reproducibility of Results White Matter - diagnostic imaging White Matter - pathology White matter hyperintensities
Title	Accuracy and reproducibility of automated white matter hyperintensities segmentation with lesion segmentation tool: A European multi-site 3T study
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