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000144980 037__ $$aDZNE-2020-00344
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000144980 1001_ $$0P:(DE-HGF)0$$aCorona, Veronica$$b0$$eCorresponding author
000144980 245__ $$aA multi-contrast MRI approach to thalamus segmentation.
000144980 260__ $$aNew York, NY$$bWiley-Liss$$c2020
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000144980 520__ $$aThalamic 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.
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000144980 542__ $$2Crossref$$i2020-01-20$$uhttp://doi.wiley.com/10.1002/tdm_license_1.1
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000144980 650_2 $$2MeSH$$aAdult
000144980 650_2 $$2MeSH$$aGray Matter: anatomy & histology
000144980 650_2 $$2MeSH$$aGray Matter: diagnostic imaging
000144980 650_2 $$2MeSH$$aHumans
000144980 650_2 $$2MeSH$$aImage Processing, Computer-Assisted
000144980 650_2 $$2MeSH$$aMagnetic Resonance Imaging: methods
000144980 650_2 $$2MeSH$$aNeuroimaging: methods
000144980 650_2 $$2MeSH$$aPattern Recognition, Automated
000144980 650_2 $$2MeSH$$aSupervised Machine Learning
000144980 650_2 $$2MeSH$$aThalamic Nuclei: anatomy & histology
000144980 650_2 $$2MeSH$$aThalamic Nuclei: diagnostic imaging
000144980 7001_ $$aLellmann, Jan$$b1
000144980 7001_ $$0P:(DE-HGF)0$$aNestor, Peter$$b2
000144980 7001_ $$aSchönlieb, Carola-Bibiane$$b3
000144980 7001_ $$0P:(DE-2719)2810751$$aAcosta-Cabronero, Julio$$b4$$eLast author$$udzne
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000144980 773__ $$0PERI:(DE-600)1492703-2$$a10.1002/hbm.24933$$gVol. 41, no. 8, p. 2104 - 2120$$n8$$p2104-2120$$tHuman brain mapping$$v41$$x1065-9471$$y2020
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