Automated quantification of white matter lesion in magnetic resonance imaging of patients with acute infarction

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• 作者：Lin Shi^a ; ^b ; Defeng Wang^a ; ^c ; ^{dfwang@cuhk.edu.hk} ; Shangping Liu^a ; Yuehua Pu^d ; Yilong Wang^d ; Winnie C.W. Chu^a ; Anil T. Ahuja^a ; Yongjun Wang^d ; ^{yongjunwang1962@gmail.com}
• 关键词：White matter lesion ; Magnetic resonance imaging ; Segmentation ; Acute infarction ; Mathematical morphology
• 刊名：Journal of Neuroscience Methods
• 出版年：2013
• 出版时间：15 February, 2013
• 年：2013
• 卷：213
• 期：1
• 页码：138-146
• 全文大小：1410 K

文摘

class=""h4"">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).

class=""h4"">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.

class=""h4"">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).