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A multi-contrast MRI approach to thalamus segmentation
Veronica Corona1, Jan Lellmann2, Peter Nestor3,4
1Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
Human Brain Mapping
|January 21, 2020
Summary
Accurate thalamic subregion segmentation is crucial for neurological disorders. A new multi-contrast MRI method improves accuracy over standard atlases, aiding clinical diagnosis and treatment.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Neurology
Background:
- Thalamic alterations are common in neurological disorders like Alzheimer's and Parkinson's disease.
- Accurate segmentation of thalamic subregions is clinically vital for diagnosis and treatment, including deep brain stimulation.
- Magnetic Resonance Imaging (MRI) offers detailed anatomical views but requires advanced segmentation techniques for multi-contrast data.
Purpose of the Study:
- To develop and evaluate a novel, multi-contrast MRI segmentation method for accurate delineation of thalamic subregions.
- To compare the proposed method's performance against standard atlas-based approaches.
Main Methods:
- A four-step segmentation approach involving iterative co-registration, manual template segmentation, supervised learning, and convex optimization.
- Utilized multi-modality MRI data, including T1-weighted, T2*-weighted, and quantitative susceptibility mapping (QSM).
- Investigated the impact of incorporating prior knowledge from training-template contours for enhanced accuracy and robustness.
Main Results:
- The proposed method achieved higher agreement with manual segmentation compared to the standard Morel atlas approach.
- Multi-contrast MRI data significantly improved segmentation performance.
- Incorporating prior knowledge from template contours led to highly precise multi-contrast segmentations in individual subjects.
Conclusions:
- The novel multi-contrast segmentation method offers superior accuracy for thalamic subregions compared to existing techniques.
- This approach enhances the precision of neuroimaging analysis for neurological disorders.
- The method is adaptable for various 3D imaging data and regions of interest.
