Accuracy and reproducibility of automated white matter hyperintensities segmentation with lesion segmentation tool: A European multi-site 3T study |
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| Affiliation: | 1. Virginia Institute of Neuropsychiatry, Midlothian, VA, USA;2. Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA;3. Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA, USA;4. University of Missouri at St. Louis, Berkeley, MO, USA;5. Department of Psychology and Neuroscience Center, Brigham Young University, Provo, UT, USA;1. Department of Health Technology, Technical University of Denmark, Denmark;2. Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Denmark;3. Medical Image Analysis Center (MIAC AG) and Department of Biomedical Engineering, University Basel, Switzerland;4. Department of Neurology and TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Germany;5. Department of Neurology, Copenhagen University Hospital Bispebjerg, Denmark;6. Institute for Clinical Medicine, Faculty of Medical and Health Sciences, University of Copenhagen, Denmark;7. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA;1. Taub Institute for Research on Alzheimer''s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY 10032, USA;2. Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY 10032, USA;3. Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY 10032, USA;4. Institute of Applied Simulation, School of Life Sciences and Facility Management, Zurich University of Applied Sciences, Wädenswil 8820, Switzerland;1. Dept. of Computer Architecture and Technology, University of Girona, Spain;2. Magnetic Resonance Unit, Dept. of Radiology, Vall d''Hebron University Hospital, Spain Architecture and Technology, University of Girona, Spain;3. Girona Magnetic Resonance Center, Spain;4. Multiple Sclerosis and Neuro-immunology Unit, Dr. Josep Trueta University Hospital, Spain;5. Neurology Unit, Multiple Sclerosis Centre of Catalonia (Cemcat), Vall d''Hebron University Hospital, Spain;1. School of Computer Science and Engineering, Central South University, Changsha 410083, China;2. Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China;3. Department of Pathology, Pingdingshan First People’s Hospital, Pingdingshan 467000, China;4. School of Natural and Computational Sciences, Massey University Auckland Campus, Auckland 0745, New Zealand;5. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China;6. Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon SK S7N5A9, Canada |
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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. |
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