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2018 | OriginalPaper | Chapter

Segmentation of Fetal Adipose Tissue Using Efficient CNNs for Portable Ultrasound

Authors : Sagar Vaze, Ana I. L. Namburete

Published in: Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis

Publisher: Springer International Publishing

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Abstract

Adipose tissue mass has been shown to have a strong correlation with fetal nourishment, which has consequences on health in infancy and later life. In rural areas of developing nations, ultrasound has the potential to be the key imaging modality due to its portability and cost. However, many ultrasound image analysis algorithms are not compatibly portable, with many taking several minutes to compute on modern CPUs.

The contributions of this work are threefold. Firstly, by adapting the popular U-Net, we show that CNNs can achieve excellent results in fetal adipose segmentation from ultrasound images. We then propose a reduced model, U-Ception, facilitating deployment of the algorithm on mobile devices. The U-Ception network provides a 98.4% reduction in model size for a 0.6% reduction in segmentation accuracy (mean Dice coefficient). We also demonstrate the clinical applicability of the work, showing that CNNs can be used to predict a trend between gestational age and adipose area.

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Title: Segmentation of Fetal Adipose Tissue Using Efficient CNNs for Portable Ultrasound
Authors: Sagar Vaze
Ana I. L. Namburete
Publisher: Springer International Publishing
Book: Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis
Print ISBN: 978-3-030-00806-2

Electronic ISBN: 978-3-030-00807-9

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-00807-9_6

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