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

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Fetal, Infant and Ophthalmic Medical Image Analysis (OMIA 2017, FIFI 2017)


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.

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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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Correspondence to Caroline Raynaud or Cybèle Ciofolo-Veit .

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Raynaud, C. et al. (2017). Multi-organ Detection in 3D Fetal Ultrasound with Machine Learning. In: Cardoso, M., et al. Fetal, Infant and Ophthalmic Medical Image Analysis. OMIA FIFI 2017 2017. Lecture Notes in Computer Science(), vol 10554. Springer, Cham.

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  • Print ISBN: 978-3-319-67560-2

  • Online ISBN: 978-3-319-67561-9

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