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Cortical Plate Segmentation Using CNNs in 3D Fetal Ultrasound

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1248)

Abstract

As the fetal brain develops, its surface undergoes rapid changes in shape and morphology. Variations in the emergence of the sulci on the brain surface have commonly been associated with diseased or at-risk pregnancies. Therefore, the process of surface folding is an important biomarker to characterise. Previous work has studied such changes by automatically delineating the cortical plate from MRI images. However, this has not been demonstrated from ultrasound, which is more commonly used for antenatal care. In this work we propose a novel method for segmenting the cortical plate from 3D ultrasound images using three varieties of convolutional neural networks (CNNs). Recent work has found improvements in medical image segmentations using multi-task learning with a distance transform regularizer. Here we implemented a similar method but found it was outperformed by the U-Net, which was able to segment the cortical plate with a Dice score of \(0.81\,\pm \,0.06\).

Keywords

3D ultrasound Fetal brain Gyrification Segmentation U-net Multi-task learning CNN 

Notes

Acknowledgements

MW is supported by the Engineering and Physical Sciences Research Council (EPSRC) and Medical Research Council (MRC) [grant number EP/L016052/1]. MJ is supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), and this research was funded by the Well- come Trust [215573/Z/19/Z]. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust [203139/Z/16/Z]. AN is grateful for support from the UK Royal Academy of Engineering under the Engineering for Development Research Fellowships scheme.

References

  1. 1.
    Chen, X., et al.: Ultrasonographic characteristics of cortical sulcus development in the human fetus between 18 and 41 weeks of gestation. Chin. Med. J. 130(8), 920 (2017)CrossRefGoogle Scholar
  2. 2.
    Chi, J.G., Dooling, E.C., Gilles, F.H.: Gyral development of the human brain. Ann. Neurol. 1(1), 86–93 (1977).  https://doi.org/10.1002/ana.410010109CrossRefGoogle Scholar
  3. 3.
    Chung, Y.S., Hyatt, C.J., Stevens, M.C.: Adolescent maturation of the relationship between cortical gyrification and cognitive ability. NeuroImage 158, 319–331 (2017)CrossRefGoogle Scholar
  4. 4.
    Clouchoux, C., Guizard, N., Evans, A.C., Du Plessis, A.J., Limperopoulos, C.: Normative fetal brain growth by quantitative in vivo magnetic resonance imaging. Am. J. Obstet. Gynecol. (2012).  https://doi.org/10.1016/j.ajog.2011.10.002CrossRefGoogle Scholar
  5. 5.
    Clouchoux, C., et al.: Quantitative in vivo MRI measurement of cortical development in the fetus. Brain Struct. Funct. 217(1), 127–139 (2012)CrossRefGoogle Scholar
  6. 6.
    Corbett-Detig, J., et al.: 3D global and regional patterns of human fetal subplate growth determined in utero. Brain Struct. Funct. 215(3–4), 255–263 (2011)CrossRefGoogle Scholar
  7. 7.
    Dangi, S., Linte, C.A., Yaniv, Z.: A distance map regularized cnn for cardiac cine mr image segmentation. Med. Phys. 46(12), 5637–5651 (2019)CrossRefGoogle Scholar
  8. 8.
    Dubois, J., et al.: Mapping the early cortical folding process in the preterm newborn brain. Cerebral Cortex 18(6), 1444–1454 (2008).  https://doi.org/10.1093/cercor/bhm180
  9. 9.
    Fernández, V., Llinares-Benadero, C., Borrell, V.: Cerebral cortex expansion and folding: what have we learned? The EMBO J. 35(10), 1021–1044 (2016).  https://doi.org/10.15252/embj.201593701
  10. 10.
    Garel, C., et al.: Fetal cerebral cortex: normal gestational landmarks identified using prenatal mr imaging. Am. J. Neuroradiol. 22(1), 184–189 (2001)Google Scholar
  11. 11.
    Guizard, N., Lepage, C., Fonov, V., Hakyemez, H., Evans, A., Limperopoulos, C.: Development of fetus brain atlas from multi-axial MR acquisitions. In: Proceedings of the Sixteenth Annual Meeting of the International Society for Magnetic Resonance in Medicine, vol. 672, p. 132 (2008)Google Scholar
  12. 12.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  13. 13.
    Lefèvre, J., et al.: Are developmental trajectories of cortical folding comparable between cross-sectional datasets of fetuses and preterm newborns? Cereb. Cortex 26(7), 3023–3035 (2016).  https://doi.org/10.1093/cercor/bhv123. https://academic.oup.com/cercor/article-lookup/doi/10.1093/cercor/bhv123
  14. 14.
    Mangin, J.F., Lopez-Krahe, J.: From 3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations. Technical report (1995)Google Scholar
  15. 15.
    Namburete, A.I.L., van Kampen, R., Papageorghiou, A.T., Papież, B.W.: Multi-channel groupwise registration to construct an ultrasound-specific fetal brain atlas. In: Melbourne, A., et al. (eds.) PIPPI/DATRA-2018. LNCS, vol. 11076, pp. 76–86. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00807-9_8CrossRefGoogle Scholar
  16. 16.
    Namburete, A.I., Xie, W., Yaqub, M., Zisserman, A., Noble, J.A.: Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning. Med. Image Anal. 46, 1–14 (2018)CrossRefGoogle Scholar
  17. 17.
    Paladini, D., Malinger, G., Monteagudo, A., Pilu, G., Timor-Tritsch, I., Toi, A.: Sonographic examination of the fetal central nervous system: guidelines for performing the ‘basic examination’ and the ‘fetal neurosonogram’. Ultrasound Obstet. Gynecol. 29(1), 109–116 (2007)CrossRefGoogle Scholar
  18. 18.
    Papageorghiou, A.T., et al.: International standards for fetal growth based on serial ultrasound measurements: the fetal growth longitudinal study of the intergrowth-21st project. Lancet 384(9946), 869–879 (2014)CrossRefGoogle Scholar
  19. 19.
    Paszke, A., et al.: Automatic differentiation in PyTorch (2017)Google Scholar
  20. 20.
    Poon, L.C., et al.: Transvaginal three-dimensional ultrasound assessment of sylvian fissures at 18–30 weeks’ gestation. Ultrasound Obstet. Gynecol. 54(2), 190–198 (2019)CrossRefGoogle Scholar
  21. 21.
    Rajagopalan, V., et al.: Local tissue growth patterns underlying normal fetal human brain gyrification quantified in utero. J. Neurosci. 31(8), 2878–2887 (2011)CrossRefGoogle Scholar
  22. 22.
    Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation, May 2015. http://arxiv.org/abs/1505.04597
  23. 23.
    Striedter, G.F., Srinivasan, S., Monuki, E.S.: Cortical folding: when, where, how, and why? Annu. Rev. Neurosci. 38(1), 291–307 (2015)CrossRefGoogle Scholar
  24. 24.
    Sun, T., Hevner, R.F.: Growth and folding of the mammalian cerebral cortex: from molecules to malformations, April 2014.  https://doi.org/10.1038/nrn3707
  25. 25.
    Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)
  26. 26.
    Yushkevich, P.A., et al.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (2006)CrossRefGoogle Scholar
  27. 27.
    Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS-2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00889-5_1CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
  2. 2.Wellcome Centre for Integrative Neuroimaging, FMRIBUniversity of OxfordOxfordUK
  3. 3.Australian Institute for Machine Learning (AIML), Department of Computer ScienceUniversity of AdelaideAdelaideAustralia
  4. 4.South Australian Health and Medical Research Institute (SAHMRI)AdelaideAustralia

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