Comprehensive segmentation of deep grey nuclei from structural MRI data
Journal:
arXiv
Published Date:
Mar 27, 2025
Abstract
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.
