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

  • Hosuk RyouEmail author
  • Mohammad Yaqub
  • Angelo Cavallaro
  • Fenella Roseman
  • Aris Papageorghiou
  • J. Alison Noble
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)

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.

Keywords

3D ultrasound First-trimester scan Random Forests Convolutional Neural Networks Fetal plane localization 

Supplementary material

432536_1_En_24_MOESM1_ESM.avi (6 mb)
Supplementary material 1 (AVI 6143 kb)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Hosuk Ryou
    • 1
    Email author
  • Mohammad Yaqub
    • 1
  • Angelo Cavallaro
    • 2
  • Fenella Roseman
    • 2
  • Aris Papageorghiou
    • 2
  • J. Alison Noble
    • 1
  1. 1.Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
  2. 2.Nuffield Department of Obstetrics and GynaecologyUniversity of OxfordOxfordUK

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