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Updated: Aug 6, 2026
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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Toward reliable thalamic segmentation: an evaluation of automated methods for structural MRI
Georgios P D Argyropoulos1,2, Christopher R Butler3,4,5, Manojkumar Saranathan6
1Memory Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK. georgios.argyropoulos@stir.ac.uk.
Brain Structure & Function
|July 18, 2026
Summary
HIPS-THOMAS offers superior automated thalamic nuclear segmentation compared to FreeSurfer methods in structural MRI analysis. This improves accuracy in brain imaging studies and clinical applications.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Radiology
Background:
- Automated thalamic nuclear segmentation is crucial for understanding brain circuits.
- Current methods like FreeSurfer struggle with intrathalamic contrast on T1-weighted MRIs.
- Limited comparisons exist between existing segmentation techniques and their atlases.
Purpose of the Study:
- To compare the performance of HIPS-THOMAS, standard FreeSurfer, and a newer FreeSurfer update for thalamic segmentation.
- To evaluate these methods across two healthy adult cohorts and one patient cohort with autoimmune limbic encephalitis.
- To provide recommendations for automated thalamic segmentation using structural brain imaging.
Main Methods:
- Comparative analysis of HIPS-THOMAS, standard FreeSurfer, and diffusion-based FreeSurfer.
- Utilized two thalamic atlases for assessment.
- Included two cohorts of healthy adults and one cohort of patients with chronic autoimmune limbic encephalitis.
Main Results:
- HIPS-THOMAS outperformed both standard and diffusion-based FreeSurfer in healthy adults.
- Improvements from the diffusion-based FreeSurfer update were minimal, affecting only a few nuclei.
- Standard FreeSurfer showed poor performance in differentiating patients from controls based on specific thalamic nuclei.
Conclusions:
- HIPS-THOMAS is a more accurate method for automated thalamic nuclear segmentation than current FreeSurfer approaches.
- Standard FreeSurfer's limitations impact clinical utility, particularly in distinguishing patient groups.
- Recommendations are provided for selecting optimal automated segmentation methods for the human thalamus.
