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International Workshop on Ophthalmic Medical Image Analysis

International Workshop on Fetal and Infant Image Analysis

OMIA 2017, FIFI 2017: Fetal, Infant and Ophthalmic Medical Image Analysis pp 62–72Cite as

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

Part of the Lecture Notes in Computer Science book series (LNIP,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

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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.

Author information

Authors and Affiliations

  1. Philips Research MediSys, Paris, France

    Caroline Raynaud, Cybèle Ciofolo-Veit, Thierry Lefèvre, Roberto Ardon & Laurence Rouet

  2. Nuffield Department of Obstetrics and Gynaecology, University of Oxford, Oxford, England

    Angelo Cavallaro, Ibtisam Salim & Aris Papageorghiou

Authors
  1. Caroline Raynaud
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  2. Cybèle Ciofolo-Veit
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  3. Thierry Lefèvre
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  4. Roberto Ardon
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  5. Angelo Cavallaro
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  6. Ibtisam Salim
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  7. Aris Papageorghiou
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  8. Laurence Rouet
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Corresponding authors

Correspondence to Caroline Raynaud or Cybèle Ciofolo-Veit .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    M. Jorge Cardoso

  2. McGill University, Montreal, Canada

    Tal Arbel

  3. University College London, London, United Kingdom

    Dr. Andrew Melbourne

  4. Medical University of Vienna, Vienna, Austria

    Dr. Hrvoje Bogunovic

  5. Eindhoven University of Technology, Eindhoven, The Netherlands

    Pim Moeskops

  6. Soochow University, Suzhou, China

    Prof. Xinjian Chen

  7. Medical University of Vienna, Vienna, Austria

    Ernst Schwartz

  8. University of Iowa, Iowa City, Iowa, USA

    Mona Garvin

  9. Imperial College London, London, United Kingdom

    Emma Robinson

  10. University of Dundee, Dundee, United Kingdom

    Emanuele Trucco

  11. University College London, London, United Kingdom

    Michael Ebner

  12. A*STAR Institute for Infocomm Research, Singapore, Singapore

    Dr. Yanwu Xu

  13. Imperial College London, London, United Kingdom

    Antonios Makropoulos

  14. University College London, London, United Kingdom

    Adrien Desjardin

  15. University College London, London, United Kingdom

    Tom Vercauteren

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Raynaud, C. et al. (2017). Multi-organ Detection in 3D Fetal Ultrasound with Machine Learning. In: , et al. Fetal, Infant and Ophthalmic Medical Image Analysis. OMIA FIFI 2017 2017. Lecture Notes in Computer Science(), vol 10554. Springer, Cham. https://doi.org/10.1007/978-3-319-67561-9_7

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  • DOI: https://doi.org/10.1007/978-3-319-67561-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67560-2

  • Online ISBN: 978-3-319-67561-9

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