Comprehensive segmentation of deep grey nuclei from structural MRI data

Manojkumar Saranathan, Giuseppina Cogliandro, Thomas Hicks, Dianne Patterson, Behroze Vachha +1 more
3/27/2025
eess.IVcs.CV

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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.

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Cite this paper

@article{saranathan2025comprehensive,
  title  = {Comprehensive segmentation of deep grey nuclei from structural MRI data},
  author = {Manojkumar Saranathan and Giuseppina Cogliandro and Thomas Hicks and Dianne Patterson and Behroze Vachha and Alberto Cacciola},
  year   = {2025},
  eprint = {2503.21955},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.21955v1}
}

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