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Spatio-Temporal Atlas of Normal Fetal Craniofacial Feature Development and CNN-Based Ocular Biometry for Motion-Corrected Fetal MRI

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 12959)


Motion-corrected fetal magnetic resonance imaging (MRI) is widely employed in large-scale fetal brain studies. However, the current processing pipelines and spatio-temporal atlases tend to omit craniofacial structures, which are known to be linked to genetic syndromes. In this work, we present the first spatio-temporal atlas of the fetal head that includes craniofacial features and covers 21 to 36 weeks gestational age range. Additionally, we propose a fully automated pipeline for fetal ocular biometry based on a 3D convolutional neural network (CNN). The extracted biometric indices are used for the growth trajectory analysis of changes in ocular metrics for 253 normal fetal subjects from the developing human connectome project (dHCP).


  • Motion-corrected fetal MRI
  • Craniofacial features
  • Ocular measurements
  • Spatio-temporal atlas
  • Automated biometry

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  • DOI: 10.1007/978-3-030-87735-4_16
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    dHCP project:

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    MIRTK library:

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    SVRTK fetal and neonatal MRI data repository:

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    SVRTK fetal and neonatal MRI data repository:


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We thank everyone who was involved in acquisition and analysis of the datasets at the Department of Perinatal Imaging and Health at King’s College London. We thank all participating mothers.

This work was supported by 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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Correspondence to Alena Uus .

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Uus, A. et al. (2021). Spatio-Temporal Atlas of Normal Fetal Craniofacial Feature Development and CNN-Based Ocular Biometry for Motion-Corrected Fetal MRI. In: , et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis. UNSURE PIPPI 2021 2021. Lecture Notes in Computer Science(), vol 12959. Springer, Cham.

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