A multi-contrast MRI approach to thalamus segmentation

TL;DR

This paper introduces a novel multi-contrast MRI segmentation method for thalamic subregions, combining iterative registration, supervised learning, and convex optimization, significantly improving accuracy over traditional atlas-based approaches.

Contribution

The study presents a new multi-contrast segmentation technique that enhances thalamic subregion delineation using T1, T2*, and QSM data, outperforming standard methods.

Findings

Multi-contrast approach improves segmentation accuracy.

Method outperforms traditional atlas-based segmentation.

Highly precise segmentations achieved with training-template priors.

Abstract

Thalamic alterations are relevant to many neurological disorders including Alzheimer's disease, Parkinson's disease and multiple sclerosis. Routine interventions to improve symptom severity in movement disorders, for example, often consist of surgery or deep brain stimulation to diencephalic nuclei. Therefore, accurate delineation of grey matter thalamic subregions is of the upmost clinical importance. MRI is highly appropriate for structural segmentation as it provides different views of the anatomy from a single scanning session. Though with several contrasts potentially available, it is also of increasing importance to develop new image segmentation techniques that can operate multi-spectrally. We hereby propose a new segmentation method for use with multi-modality data, which we evaluated for automated segmentation of major thalamic subnuclear groups using T1-, T2*-weighted and quantitative susceptibility mapping (QSM) information. The proposed method consists of four steps: highly iterative image co-registration, manual segmentation on the average training-data template, supervised learning for pattern recognition, and a final convex optimisation step imposing further spatial constraints to refine the solution. This led to solutions in greater agreement with manual segmentation than the standard Morel atlas based approach. Furthermore, we show that the multi-contrast approach boosts segmentation performances. We then investigated whether prior knowledge using the training-template contours could further improve convex segmentation accuracy and robustness, which led to highly precise multi-contrast segmentations in single subjects. This approach can be extended to most 3D imaging data types and any region of interest discernible in single scans or multi-subject templates.