Brain tissue segmentation based on MP2RAGE multi-contrast images in 7 T MRI

Author:

Choi Uk-Su,Kawaguchi Hirokazu,Matsuoka Yuichiro,Kober Tobias,Kida Ikuhiro

Abstract

AbstractWe proposed a method for segmentation of brain tissues––gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF)—using multi-contrast images, including a T1 map and a uniform T1-weighted image, from a magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE) sequence at 7 Tesla. The proposed method was evaluated with respect to the processing time and the similarity of the segmented masks of brain tissues with those obtained using FreeSurfer, FSL, and SPM12. The processing time of the proposed method (28 ± 0 s) was significantly shorter than those of FSL and SPM12 (444 ± 4 s and 159 ± 2 s for FSL and SPM12, respectively). In the similarity assessment, the tissue mask of the brain obtained by the proposed method showed higher consistency with those obtained by FSL than with those obtained by SPM12. The proposed method misclassified the subcortical structures and large vessels since it is based on the intensities of multi-contrast images obtained using MP2RAGE, which uses a similar segmentation approach as FSL but is not based on a template image or a parcellated brain atlas, which are used for FreeSurfer and SPM12, respectively. However, the proposed method showed good segmentation in the cerebellum and WM in the medial part of the brain in comparison with the other methods. Thus, because the proposed method using different contrast images of MP2RAGE sequence showed the shortest processing time and similar segmentation ability as the other methods, it may be useful for both neuroimaging research and clinical diagnosis.

Publisher

Cold Spring Harbor Laboratory

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An atlas-free newborn brain image segmentation and classification scheme based on SOM-DCNN with sparse auto encoder;Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization;2019-04-28

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