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Multi-organ Detection in 3D Fetal Ultrasound with Machine Learning

  • Caroline RaynaudEmail author
  • Cybèle Ciofolo-VeitEmail author
  • Thierry Lefèvre
  • Roberto Ardon
  • Angelo Cavallaro
  • Ibtisam Salim
  • Aris Papageorghiou
  • Laurence Rouet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10554)

Abstract

3D ultrasound (US) is a promising technique to perform automatic extraction of standard planes for fetal anatomy assessment. This requires prior organ localization, which is difficult to obtain with direct learning approaches because of the high variability in fetus size and orientation in US volumes. In this paper, we propose a methodology to overcome this spatial variability issue by scaling and automatically aligning volumes in a common 3D reference coordinate system. This preprocessing allows the organ detection algorithm to learn features that only encodes the anatomical variability while discarding the fetus pose. All steps of the approach are evaluated on 126 manually annotated volumes, with an overall mean localization error of 11.9 mm, showing the feasibility of multi-organ detection in 3D fetal US with machine learning.

Keywords

3D ultrasound Volume alignment Landmark localization 

Notes

Acknowledgements

This work was done in Philips Research Paris (MediSys), with images acquired and manually annotated at the John Radcliffe Hospital, Oxford, in collaboration with the University of Oxford, with funding from Philips Ultrasound.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Caroline Raynaud
    • 1
    Email author
  • Cybèle Ciofolo-Veit
    • 1
    Email author
  • Thierry Lefèvre
    • 1
  • Roberto Ardon
    • 1
  • Angelo Cavallaro
    • 2
  • Ibtisam Salim
    • 2
  • Aris Papageorghiou
    • 2
  • Laurence Rouet
    • 1
  1. 1.Philips Research MediSysParisFrance
  2. 2.Nuffield Department of Obstetrics and GynaecologyUniversity of OxfordOxfordEngland

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