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[][license]
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# Morphological atlas of neonatal brain development
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-<div align="center">
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- <img src="figures/overview.png" alt="Atlas images" width="90%"/>
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-</div>
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Spatio-temporal neonatal brain atlas constructed as part of the [Developing Human Connectome Project (dHCP)][dhcp].
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The methodology used to built this temporally consistent atlas was first presented in [Schuh, 2017][Schuh2017-hdl] and [Schuh *et al.*, 2018][Schuh2018-doi].
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@@ -71,9 +68,7 @@ T2-weighted images were obtained in two stacks of 2D slices using a Turbo Spin E
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The histograms below show the distribution of gestational age at birth (steps) and post-menstrual age at scan (bars) of the dataset consisting of 275 T1-weighted and T2-weighted brain images used to construct this spatio-temporal brain atlas.
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-<div align="center">
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- <img src="figures/distribution.png" alt="Age distribution" width="480px"/>
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## Methods
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@@ -81,17 +76,9 @@ The histograms below show the distribution of gestational age at birth (steps) a
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All reconstructed volumes were preprocessed using a pipeline designed for neonatal brain images, including an initial rough brain extraction and bias field correction. The images were segmented into 87 regions of interest (ROIs) and 9 tissue classes (background, CSF, cGM, dGM, WM, ventricles, cerebellum, brainstem, hippocampi and amygdalae) using an extension of the [Developing Brain Region Annotation With Expectation-Maximization (Draw-EM)][drawem] algorithm. A detailed description of this pipeline is given in [Makropoulos *et al.*, 2018][Makropoulos2018-doi], and the [source code][Makropoulos2018-src] is available on GitHub.
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The spatio-temporal neonatal brain atlas construction is summarised in [Schuh *et al.*, 2018][Schuh2018-doi].
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-<div align="center">
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- <img src="figures/construction.png" alt="Atlas construction" width="640px"/>
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-</div>
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-<div align="center">
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- <img src="figures/deformations.png" alt="Image deformations" width="480px"/>
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-</div>
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Further details, including an evaluation of the employed diffeomorphic registration method, can be found in [Schuh, 2017][Schuh2017-hdl].
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