TY  - JOUR
AU  - Corona, Veronica
AU  - Lellmann, Jan
AU  - Nestor, Peter
AU  - Schönlieb, Carola-Bibiane
AU  - Acosta-Cabronero, Julio
TI  - A multi-contrast MRI approach to thalamus segmentation.
JO  - Human brain mapping
VL  - 41
IS  - 8
SN  - 1065-9471
CY  - New York, NY
PB  - Wiley-Liss
M1  - DZNE-2020-00344
SP  - 2104-2120
PY  - 2020
AB  - 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.
KW  - Adult
KW  - Gray Matter: anatomy & histology
KW  - Gray Matter: diagnostic imaging
KW  - Humans
KW  - Image Processing, Computer-Assisted
KW  - Magnetic Resonance Imaging: methods
KW  - Neuroimaging: methods
KW  - Pattern Recognition, Automated
KW  - Supervised Machine Learning
KW  - Thalamic Nuclei: anatomy & histology
KW  - Thalamic Nuclei: diagnostic imaging
LB  - PUB:(DE-HGF)16
C6  - pmid:31957926
C2  - pmc:PMC7267924
DO  - DOI:10.1002/hbm.24933
UR  - https://pub.dzne.de/record/144980
ER  -