Comprehensive Segmentation of Deep Grey Nuclei From Structural MRI Data

Abstract

There is a lack of tools for comprehensive and complete segmentation of deep grey nuclei using a single software for reproducibility and repeatability. We present a fast, accurate, and robust method for segmentation of deep grey nuclei (thalamic nuclei, basal ganglia, amygdala, claustrum, and red nucleus) from structural T1 MRI data at conventional field strengths. We leveraged the improved contrast of white-matter-nulled imaging by using the recently proposed Histogram-based Polynomial Synthesis (HIPS) to synthesize white-matter nulled images from standard T1 and then use a multi-atlas segmentation with joint label fusion to segment deep grey nuclei. The method worked robustly on all field strengths (1.5/3/7T) and Dice coefficients ≥ 0.7 were achieved for all structures compared against manual segmentation ground truth. In conclusion, this method facilitates careful investigation 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.