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Identifying High Order Brain Connectome Biomarkers via Learning on Hypergraph Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Automated 3D Ultrasound Biometry Planes Extraction for First Trimester Fetal Assessment

Authors : Hosuk Ryou, Mohammad Yaqub, Angelo Cavallaro, Fenella Roseman, Aris Papageorghiou, J. Alison Noble

Published in: Machine Learning in Medical Imaging

Publisher: Springer International Publishing

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Abstract

In this paper, we present a fully automated machine-learning based solution to localize the fetus and extract the best fetal biometry planes for the head and abdomen from 11–13+6days week 3D fetal ultrasound (US) images. Our method to localize the whole fetus in the sagittal plane utilizes Structured Random Forests (SRFs) and classical Random Forests (RFs). A transfer learning Convolutional Neural Network (CNNs) is then applied to axial images to localize one of three classes (head, body and non-fetal). Finally, the best fetal head and abdomen planes are automatically extracted based on clinical knowledge of the position of the fetal biometry planes within the head and body. Our hybrid method achieves promising localization of the best biometry fetal planes with 1.6 mm and 3.4 mm for head and abdomen plane localization respectively compared to the best manually chosen biometry planes.

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Appendix
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Metadata
Title
Automated 3D Ultrasound Biometry Planes Extraction for First Trimester Fetal Assessment
Authors
Hosuk Ryou
Mohammad Yaqub
Angelo Cavallaro
Fenella Roseman
Aris Papageorghiou
J. Alison Noble
Copyright Year
2016
Book
Machine Learning in Medical Imaging

Print ISBN: 978-3-319-47156-3

Electronic ISBN: 978-3-319-47157-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-47157-0_24

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