Abstract
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).
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Notes
- 1.
dHCP project: http://www.developingconnectome.org/project.
- 2.
MIRTK library: https://github.com/BioMedIA/MIRTK.
- 3.
PyTorch: https://pytorch.org.
- 4.
SVRTK fetal and neonatal MRI data repository: https://gin.g-node.org/SVRTK.
- 5.
SVRTK fetal and neonatal MRI data repository: https://gin.g-node.org/SVRTK.
References
Ami, O., et al.: 3D magnetic resonance imaging of fetal head molding and brain shape changes during the second stage of labor. PLoS ONE 14(5) (2019)
Arangio, P., et al.: Importance of fetal MRI in evaluation of craniofacial deformities. J. Craniofac. Surg. 24(3), 773–776 (2013)
Avisdris, N., et al.: Automatic fetal ocular measurements in MRI. In: ISMRM 2021, p. 1190 (2021)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Cordero-Grande1, L., et al.: Automating motion compensation in 3T fetal brain imaging: localize, align and reconstruct. In: ISMRM 2019, p. 1000 (2019)
Ebner, M., et al.: An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. Neuroimage 206(Oct.) (2020)
Ettema, A., et al.: Prenatal diagnosis of craniomaxillofacial malformations: a characterization of phenotypes in trisomies 13, 18, and 21 by ultrasound and pathology. Cleft Palate-Craniofac. J. 47(2), 189–196 (2010)
Gholipour, A., et al.: A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth. Nat. Sci. Rep. 7(476), 1–13 (2017)
Grigorescu, I., et al.: Harmonized segmentation of neonatal brain MRI. Front. Neurosci. 15, 565 (2021)
Khalili, N., et al.: Automatic brain tissue segmentation in fetal MRI using convolutional neural networks. Magn. Reson. Imaging 64, 77–89 (2019)
Kuklisova-Murgasova, M., et al.: Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. MedIAn 16(8), 1550–1564 (2012)
Kul, S., et al.: Contribution of MRI to ultrasound in the diagnosis of fetal anomalies. J. Magn. Reson. Imaging 35(4), 882–890 (2012)
Kyriakopoulou, V., et al.: Normative biometry of the fetal brain using magnetic resonance imaging. Brain Struct. Funct. 222(5), 2295–2307 (2016). https://doi.org/10.1007/s00429-016-1342-6
Makropoulos, A., et al.: The dHCP: a minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 173, 88–112 (2018)
Mossey, P., Castilla, E.E.: Global registry and database on craniofacial anomalies Report of a WHO Registry Meeting on Craniofacial Anomalies Human Genetics Programme Management of Noncommunicable Diseases (2003)
Payette, K., Kottke, R., Jakab, A.: Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels. In: Hu, Y., et al. (eds.) ASMUS/PIPPI -2020. LNCS, vol. 12437, pp. 295–304. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60334-2_29
Pérez-García, F., et al.: TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. arXiv (March 2020)
Price, A., et al.: The developing Human Connectome Project (dHCP): fetal acquisition protocol. In: ISMRM 2019 (2019)
Robinson, A.J., et al.: MRI of the fetal eyes: morphologic and biometric assessment for abnormal development with ultrasonographic and clinicopathologic correlation. Pediatr. Radiol. 38(9), 971–981 (2008)
Schuh, A., et al.: Unbiased construction of a temporally consistent morphological atlas of neonatal brain development. bioRxiv (2018)
Velasco-Annis, C., et al.: Normative biometrics for fetal ocular growth using volumetric MRI reconstruction. Prenat. Diagn. 35(4), 400–408 (2015)
Wright, R., et al.: LSTM spatial co-transformer networks for registration of 3D fetal US and MR brain images. MICCAI 2018, 107–116 (2018)
Acknowledgments
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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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: Sudre, C.H., 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. https://doi.org/10.1007/978-3-030-87735-4_16
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