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• W4313451764 abstract "Abstract The human brain undergoes major developmental changes during pregnancy. Three-dimensional (3D) ultrasound images allow for the opportunity to investigate typical prenatal brain development on a large scale. Here, we developed a convolutional neural network (CNN) model for automated segmentation of fetal intracranial volume (ICV) from 3D ultrasound, and we applied the trained model in a large independent sample (N = 9795 ultrasounds; N=1763 participants) from the YOUth Baby and Child cohort measured at 20- and 30-week of gestational age to investigate sex differences in fetal ICV and ICV growth. 168 3D ultrasound images with ICV annotations were included to develop a 3D CNN model for automated fetal ICV segmentation. A data augmentation strategy provided physical variation and additional data to train the network. K-fold cross-validation and Bayesian optimization were used for network selection and the ensemble-based system combined multiple networks to form the final ensemble network. The final ensemble network produced consistent and high-quality segmentations of ICV. Our trained model successfully predicted ICV and ICV growth in a large independent longitudinal data set. Boys had significantly larger ICV at 20- (B=2.83; p =1.4e-13) and 30-weeks of pregnancy (B=12.01; p =2.0e-28), and more pronounced ICV growth than girls (t=-4.395; p =1.3e-5). Our approach provides us with the opportunity to investigate fetal brain development on a much larger scale and to answer fundamental questions related to prenatal brain development." @default.
• W4313451764 created "2023-01-06" @default.
• W4313451764 creator A5002297531 @default.
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• W4313451764 date "2022-12-20" @default.
• W4313451764 modified "2023-09-26" @default.
• W4313451764 title "Automated segmentation of fetal intracranial volume in 3D ultrasound using deep learning: identifying sex differences in prenatal brain development" @default.
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• W4313451764 doi "https://doi.org/10.1101/2022.12.19.521094" @default.
• W4313451764 hasPublicationYear "2022" @default.
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