PURPOSE: It has been reported that increased white matter lesions (WML) is one of the risk factors for stroke. To quantify WML objectively with the presence of acute infarcts, we proposed an automated segmentation scheme to locate WMLs in combined T1-weighted MRI, fluid attenuation inversion recovery (FLAIR) and diffusion weighted imaging (DWI). MATERIALS AND METHODS: The proposed method detects WMLs by a coarse-to-fine mathematical morphology method. It has been evaluated quantitatively and qualitatively using voxel-based, volume-based, score-based, and atlas-based approaches on MRI data of 91 subjects with acute infarction. RESULT: The proposed WML detection algorithm yields average sensitivity, positive predictive value and similarity index of 0.803, 0.818, and 0.836, respectively. Experimental results demonstrated that the segmentation from the proposed method is in high agreement with that from manual segmentation (intraclass correlation coefficient=0.9892), and with a good correlation with visual scores (R=0.8442, p<0.0001).
PURPOSE: It has been reported that increased white matter lesions (WML) is one of the risk factors for stroke. To quantify WML objectively with the presence of acute infarcts, we proposed an automated segmentation scheme to locate WMLs in combined T1-weighted MRI, fluid attenuation inversion recovery (FLAIR) and diffusion weighted imaging (DWI). MATERIALS AND METHODS: The proposed method detects WMLs by a coarse-to-fine mathematical morphology method. It has been evaluated quantitatively and qualitatively using voxel-based, volume-based, score-based, and atlas-based approaches on MRI data of 91 subjects with acute infarction. RESULT: The proposed WML detection algorithm yields average sensitivity, positive predictive value and similarity index of 0.803, 0.818, and 0.836, respectively. Experimental results demonstrated that the segmentation from the proposed method is in high agreement with that from manual segmentation (intraclass correlation coefficient=0.9892), and with a good correlation with visual scores (R=0.8442, p<0.0001).
Authors: Zhiyu Cao; Yingren Mai; Wenli Fang; Ming Lei; Yishan Luo; Lei Zhao; Wang Liao; Qun Yu; Jiaxin Xu; Yuting Ruan; Songhua Xiao; Vincent C T Mok; Lin Shi; Jun Liu Journal: Front Hum Neurosci Date: 2022-06-14 Impact factor: 3.473
Authors: Maria Eugenia Caligiuri; Paolo Perrotta; Antonio Augimeri; Federico Rocca; Aldo Quattrone; Andrea Cherubini Journal: Neuroinformatics Date: 2015-07
Authors: Mohsen Ghafoorian; Nico Karssemeijer; Tom Heskes; Inge W M van Uden; Clara I Sanchez; Geert Litjens; Frank-Erik de Leeuw; Bram van Ginneken; Elena Marchiori; Bram Platel Journal: Sci Rep Date: 2017-07-11 Impact factor: 4.379
Authors: Lin Shi; Xinyuan Miao; Wutao Lou; Kai Liu; Jill Abrigo; Adrian Wong; Winnie C W Chu; Defeng Wang; Vincent C T Mok Journal: Front Neurol Date: 2017-11-09 Impact factor: 4.003
Authors: Lei Zhao; Adrian Wong; Yishan Luo; Wenyan Liu; Winnie W C Chu; Jill M Abrigo; Ryan K L Lee; Vincent Mok; Lin Shi Journal: Front Neurosci Date: 2018-05-01 Impact factor: 4.677