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)


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 \(0.81\,\pm \,0.06\).


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



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.


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© 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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