Sparse representation of DWI images for fully automated brain tissue segmentation.,Journal of Neuroscience Methods

当前位置： X-MOL 学术 › J. Neurosci. Methods › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Sparse representation of DWI images for fully automated brain tissue segmentation.
Journal of Neuroscience Methods ( IF 2.7 ) Pub Date : 2020-06-27 , DOI: 10.1016/j.jneumeth.2020.108828
Jian Wang ₁ , Hu Cheng ₂ , Sharlene D Newman ₂

Affiliation

Background

Brain tissue segmentation plays an important role in biomedical research and clinical applications. Traditional segmentation is performed on T1-weighted and/or T2-weighted MRI images. Recently, brain segmentation based on diffusion weighted imaging (DWI) has attracted research interest due to its advantage in diffusion MRI image processing and anatomically-constrained tractography.

New method

We propose a fully automated brain segmentation method based on sparse representation of DWI signals and applied it on nine healthy subjects of Human Connectome Project aged 25–35 years. Learning a dictionary from DWI signals of each subject, brain voxels are classified into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) according to their sparse representation of clustered dictionary atoms, achieving good agreement with the segmentation on T1-weighted images using SPM12, as assessed by the DICE score.

Results

The average DICE score for all nine subjects was 0.814 for CSF, 0.850 for GM, and 0.890 for WM. The proposed method is very fast and robust for a wide range of sparse coding parameter selection. It also works well on DWI data with less number of shells or gradient directions.

Comparison with existing methods

On average, our segmentation results are superior to previous methods for all three brain tissue classes in terms of DICE scores.

Conclusion

The proposed method demonstrates the feasibility of segmenting the brain solely based on the tissue response to diffusion encoding.

中文翻译：

用于全自动脑组织分割的DWI图像的稀疏表示。

背景

脑组织分割在生物医学研究和临床应用中起着重要作用。传统分割是在T1加权和/或T2加权MRI图像上执行的。近年来，基于弥散加权成像（DWI）的脑分割技术由于其在弥散MRI图像处理和解剖学约束的术中的优势而吸引了研究兴趣。

新方法

我们提出了一种基于DWI信号稀疏表示的全自动大脑分割方法，并将其应用于25-35岁的人类Connectome项目的9位健康受试者。从每个对象的DWI信号中学习字典，根据其簇字典原子的稀疏表示，将大脑体素分为灰质（GM），白质（WM）和脑脊髓液（CSF），与在使用DPM得分评估的使用SPM12的T1加权图像。