Sparse representation of DWI images for fully automated brain tissue segmentation

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.