Computer-aided evaluation method of white matter hyperintensities related to subcortical vascular dementia based on magnetic resonance imaging |
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| Authors: | Yasuo Kawata Hidetaka Arimura Yasuo Yamashita Taiki Magome Masafumi Ohki Fukai Toyofuku Yoshiharu Higashida Kazuhiro Tsuchiya |
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| Institution: | 1. Graduate School of Medical Science, Kyushu University, Fukuoka 812-8582, Japan;2. Faculty of Medical Science, Kyushu University, Fukuoka 812-8582, Japan;3. Department of Radiology, Kyorin University, Mitaka, Tokyo 181-8611, Japan;1. Dementia Research Centre, Department of Neurodegenerative Disease, University College London Institute of Neurology, London, UK;2. Department of Neurology, University of California–Davis, Davis, CA, USA;3. Wellcome Trust Centre for Neuroimaging, University College London Institute of Neurology, London, UK;4. Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK;5. Department of Neurology and Neurosurgery, University Medical Center Utrecht, the Netherlands;1. Vascular and Interventional Radiology Unit, Department of Radiological, Oncological and Anatomo-pathological Sciences, Sapienza University of Rome, Rome, Italy;2. Department of Surgery, Section of Vascular Surgery, Azienda Ospedaliero Universitaria (AOU) of Cagliari-Polo di Monserrato, Cagliari, Italy;3. Diagnostic and Monitoring Department, AtheroPoint LLC, Roseville, CA, USA;4. Department of Medical Imaging, Azienda Ospedaliero Universitaria (AOU) of Cagliari-Polo di Monserrato, Cagliari, Italy;5. Center for Life Nano Science@Sapienza, Istituto Italiano di Tecnologia, Sapienza University, Rome, Italy;1. Department of Neurology, The Holy Family Specialist Hospital, Rudna Mala, Rzeszow, Poland;2. Clinical Department of Radiology, Provincial Hospital No. 2, named after St. Jadwiga the Queen, Rzeszow, Poland;3. Department of Neurology, Medical University of Warsaw, Poland;1. Centre of Brain Aging, Neurology Unit, Department of Biomedical Sciences and Translational Medicine, University of Brescia, Brescia, Italy;2. Centre of Brain Aging, Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy;3. The III Laboratory, Biotechnology, Spedali Civili Hospital, Brescia, Italy;4. Neurology Unit, Valle Camonica Hospital, Brescia, Italy;5. Casa di Cura San Francesco, Bergamo, Italy;6. The Neuroradiology Unit, University of Brescia, Brescia, Italy;1. School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Room 123, 3 Teaching Building, No. 1954, Huashan Rd, Shanghai 200030, China;2. Institute of Diagnostic and Interventional Radiology, Sixth Affiliated People''s Hospital, Shanghai Jiao Tong University, Shanghai, China;3. CREATICS, CNRS UMR 5220, Inserm 1044, INSA Lyon, Villeurbanne, France;1. J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA;2. Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA;3. Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA |
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| Abstract: | It has been reported that the severity of subcortical vascular dementia (VaD) correlated with an area ratio of white matter hyperintensity (WMH) regions to the brain parenchyma (WMH area ratio). The purpose of this study was to develop a computer-aided evaluation method of WMH regions for diagnosis of subcortical VaD based on magnetic resonance (MR) images. A brain parenchymal region was segmented based on the histogram analysis of a T1-weigthed image. The WMH regions were segmented on the subtraction image between a T1-weighted and fluid-attenuated inversion-recovery (FLAIR) images using two segmentation methods, i.e., a region-growing technique and a level-set method, which were automatically and adaptively selected on each WMH region based on its image features by using a support vector machine. We applied the proposed method to 33 slices of the three types of MR images with 245 lesions, which were acquired from 10 patients (age range: 64–90 years, mean: 78) with a diagnosis of VaD on a 1.5-T MR imaging scanner. The average similarity index between regions determined by a manual method and the proposed method was 93.5 ± 2.0% for brain parenchymal regions and 78.2 ± 11.0% for WMH regions. The WMH area ratio obtained by the proposed method correlated with that determined by two neuroradiologists with a correlation coefficient of 0.992. The results presented in this study suggest that the proposed method could assist neuroradiologists in the evaluation of WMH regions related to the subcortical VaD. |
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