Journal Article

DZNE-2020-00344

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A multi-contrast MRI approach to thalamus segmentation.

Corona, V. (Corresponding author) ; Lellmann, J. ; Nestor, P. ; Schönlieb, C.-B. ; Acosta-Cabronero, J. (Last author)DZNE*

2020
Wiley-Liss New York, NY

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Please use a persistent id in citations: doi:10.1002/hbm.24933

Abstract: Thalamic alterations occur in 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 -weighted, T 2 * -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.

Keyword(s): Adult (MeSH) ; Gray Matter: anatomy & histology (MeSH) ; Gray Matter: diagnostic imaging (MeSH) ; Humans (MeSH) ; Image Processing, Computer-Assisted (MeSH) ; Magnetic Resonance Imaging: methods (MeSH) ; Neuroimaging: methods (MeSH) ; Pattern Recognition, Automated (MeSH) ; Supervised Machine Learning (MeSH) ; Thalamic Nuclei: anatomy & histology (MeSH) ; Thalamic Nuclei: diagnostic imaging (MeSH)

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Contributing Institute(s):

1. Cognitive Neurology and Neurodegeneration (AG Nestor)

Research Program(s):

1. 344 - Clinical and Health Care Research (POF3-344) (POF3-344)

Appears in the scientific report 2020

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Database coverage:
; ; ; ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; DEAL Wiley ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 5 ; JCR ; Nationallizenz ; SCOPUS ; Web of Science Core Collection

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