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Multi-task CNN for Structural Semantic Segmentation in 3D Fetal Brain Ultrasound

Part of the Communications in Computer and Information Science book series (CCIS,volume 1065)

Abstract

The fetal brain undergoes extensive morphological changes throughout pregnancy, which can be visually seen in ultrasound acquisitions. We explore the use of convolutional neural networks (CNNs) for the segmentation of multiple fetal brain structures in 3D ultrasound images. Accurate automatic segmentation of brain structures in fetal ultrasound images can track brain development through gestation, and can provide useful information that can help predict fetal health outcomes. We propose a multi-task CNN to produce automatic segmentations from atlas-generated labels of the white matter, thalamus, brainstem, and cerebellum. The network as trained on 480 volumes produced accurate 3D segmentations on 48 test volumes, with Dice coefficient of 0.93 on the white matter and over 0.77 on segmentations of thalamus, brainstem and cerebellum.

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Notes

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    Due to the size of this network, we used a batch size of 1 volume for training.

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Acknowledgment

This work is supported by funding from the Engineering and Physical Sciences Research Council (EPSRC) and Medical Research Council (MRC) [grant number EP/L016052/1]. A. T. Papageorghiou is supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). A. Namburete is grateful for support from the UK Royal Academy of Engineering under the Engineering for Development Research Fellowships scheme. We thank the INTERGROWTH-21st Consortium for permission to use 3D ultrasound volumes of the fetal brain.

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Correspondence to Lorenzo Venturini .

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Venturini, L., Papageorghiou, A.T., Noble, J.A., Namburete, A.I.L. (2020). Multi-task CNN for Structural Semantic Segmentation in 3D Fetal Brain Ultrasound. In: Zheng, Y., Williams, B., Chen, K. (eds) Medical Image Understanding and Analysis. MIUA 2019. Communications in Computer and Information Science, vol 1065. Springer, Cham. https://doi.org/10.1007/978-3-030-39343-4_14

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  • DOI: https://doi.org/10.1007/978-3-030-39343-4_14

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