Abstract
MRI is conventionally employed in neonatal brain diagnosis and research studies. However, the traditional segmentation protocols omit differentiation between heterogeneous white matter (WM) tissue zones that rapidly evolve and change during the early brain development. There is a reported correlations of characteristics of the transient WM compartments (including periventricular regions, subplate, etc.) with brain maturation [23, 26] and neurodevelopment scores [22]. However, there are no currently available standards for parcellation of these regions in MRI scans. Therefore, in this work, we propose the first deep learning solution for automated 3D segmentation of periventricular WM (PWM) regions that would be the first step towards tissue-specific WM analysis. The implemented segmentation method based on UNETR [13] was then used for assessment of the differences between term and preterm cohorts (200 subjects) from the developing Human Connectome Project (dHCP) (dHCP) project [1] in terms of the ROI-specific volumetry and microstructural diffusion MRI indices.
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Acknowledgments
We thank everyone who was involved in acquisition and analysis of the datasets as a part of dHCP project. We thank all participants and their families.
This work was supported by the Academy of Medical Sciences Springboard Award (SBF004\(\setminus \)1040), the European Research Council under the European Union’s Seventh Framework Programme [FP7/ 20072013]/ERC grant agreement no. 319456 dHCP project, the Wellcome/EPSRC Centre for Medical Engineering at King’s College London [WT 203148/Z/16/Z)], the NIHR Clinical Research Facility (CRF) at Guy’s and St Thomas’ and by the National Institute for Health Research Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London.
The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
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Uus, A.U. et al. (2022). Segmentation of Periventricular White Matter in Neonatal Brain MRI: Analysis of Brain Maturation in Term and Preterm Cohorts. In: Licandro, R., Melbourne, A., Abaci Turk, E., Macgowan, C., Hutter, J. (eds) Perinatal, Preterm and Paediatric Image Analysis. PIPPI 2022. Lecture Notes in Computer Science, vol 13575. Springer, Cham. https://doi.org/10.1007/978-3-031-17117-8_9
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