Comprehensive segmentation of deep grey nuclei from structural MRI data
This enables reproducible segmentation of deep grey nuclei from conventional T1 data in large public databases, addressing a lack of tools for such analysis.
The authors tackled the problem of segmenting deep grey nuclei from structural MRI data by synthesizing white-matter-nulled images from standard T1 scans and using multi-atlas segmentation, achieving Dice coefficients of 0.7 or higher across field strengths.
Motivation: Lack of tools for comprehensive and complete segmentation of deep grey nuclei using a single software for reproducibility and repeatability Goal(s): A fast accurate and robust method for segmentation of deep grey nuclei (thalamic nuclei, basal ganglia, claustrum, red nucleus) from structural T1 MRI data at conventional field strengths Approach: We leverage the improved contrast of white-matter-nulled imaging by using the recently proposed Histogram-based Polynomial Synthesis (HIPS) to synthesize WMn-like images from standard T1 and then use a multi-atlas segmentation with joint label fusion to segment deep grey nuclei. Results: The method worked robustly on all field strengths (1.5/3/7) and Dice coefficients of 0.7 or more were achieved for all structures compared against manual segmentation ground truth. Impact: This method facilitates careful investigation of the role of deep grey nuclei by enabling the use of conventional T1 data from large public databases, which has not been possible, hitherto, due to lack of robust reproducible segmentation tools.