Automatic segmentation of deep grey nuclei using a high‐resolution 7T magnetic resonance imaging atlas—Quantification of T1 values in healthy volunteers
Abstract We present a new consensus atlas of deep grey nuclei obtained by shape‐based averaging of manual segmentation of two experienced neuroradiologists and optimized from 7T MP2RAGE images acquired at (.6 mm)3 in 60 healthy subjects. A group‐wise normalization method was used to build a high‐contrast and high‐resolution T1‐weighted brain template (.5 mm)3 using data from 30 out of the 60 controls. Delineation of 24 deep grey nuclei per hemisphere, including the claustrum and 12 thalamic nuclei, was then performed by two expert neuroradiologists and reviewed by a third neuroradiologist according to tissue contrast and external references based on the Morel atlas. Corresponding deep grey matter structures were also extracted from the Morel and CIT168 atlases. The data‐derived, Morel and CIT168 atlases were all applied at the individual level using non‐linear registration to fit the subject reference and to extract absolute mean quantitative T1 values derived from the 3D‐MP2RAGE volumes, after correction for residual B1+ biases. Three metrics (the Dice and the volumetric similarity coefficients and a novel Hausdorff distance) were used to estimate the inter‐rater agreement of manual MRI segmentation and inter‐atlas variability, and these metrics were measured to quantify biases due to image registration, and their impact on the measurements of the quantitative T1 values was highlighted. This represents a fully automated segmentation process permitting the extraction of unbiased normative T1 values in a population of young healthy controls as a reference for characterizing subtle structural alterations of deep grey nuclei relevant to a range of neurological diseases..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:55 |
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| Enthalten in: |
European Journal of Neuroscience - 55(2022), 2, Seite 438-460 |
| Beteiligte Personen: |
Brun, Gilles [VerfasserIn] |
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| BKL: |
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| Anmerkungen: |
© 2022 Federation of European Neuroscience Societies and John Wiley & Sons Ltd |
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| Umfang: |
23 |
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| doi: |
10.1111/ejn.15575 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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WLY005042933 |
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| 520 | |a Abstract We present a new consensus atlas of deep grey nuclei obtained by shape‐based averaging of manual segmentation of two experienced neuroradiologists and optimized from 7T MP2RAGE images acquired at (.6 mm)3 in 60 healthy subjects. A group‐wise normalization method was used to build a high‐contrast and high‐resolution T1‐weighted brain template (.5 mm)3 using data from 30 out of the 60 controls. Delineation of 24 deep grey nuclei per hemisphere, including the claustrum and 12 thalamic nuclei, was then performed by two expert neuroradiologists and reviewed by a third neuroradiologist according to tissue contrast and external references based on the Morel atlas. Corresponding deep grey matter structures were also extracted from the Morel and CIT168 atlases. The data‐derived, Morel and CIT168 atlases were all applied at the individual level using non‐linear registration to fit the subject reference and to extract absolute mean quantitative T1 values derived from the 3D‐MP2RAGE volumes, after correction for residual B1+ biases. Three metrics (the Dice and the volumetric similarity coefficients and a novel Hausdorff distance) were used to estimate the inter‐rater agreement of manual MRI segmentation and inter‐atlas variability, and these metrics were measured to quantify biases due to image registration, and their impact on the measurements of the quantitative T1 values was highlighted. This represents a fully automated segmentation process permitting the extraction of unbiased normative T1 values in a population of young healthy controls as a reference for characterizing subtle structural alterations of deep grey nuclei relevant to a range of neurological diseases. | ||
| 700 | 1 | |a Testud, Benoit |4 aut | |
| 700 | 1 | |a Girard, Olivier M. |4 aut | |
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