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Multi-channel Groupwise Registration to Construct an Ultrasound-Specific Fetal Brain Atlas

  • Ana I. L. Namburete
  • Raquel van Kampen
  • Aris T. Papageorghiou
  • Bartłomiej W. Papież
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11076)

Abstract

In this paper, we describe a method to construct a 3D atlas from fetal brain ultrasound (US) volumes. A multi-channel groupwise Demons registration is proposed to simultaneously register a set of images from a population to a common reference space, thereby representing the population average. Similar to the standard Demons formulation, our approach takes as input an intensity image, but with an additional channel which contains phase-based features extracted from the intensity channel. The proposed multi-channel atlas construction method is evaluated using a groupwise Dice overlap, and is shown to outperform standard (single-channel) groupwise diffeomorphic Demons registration. This method is then used to construct an atlas from US brain volumes collected from a population of 39 healthy fetal subjects at 23 gestational weeks. The resulting atlas manifests high structural overlap, and correspondence between the US-based and an age-matched fetal MRI-based atlas is observed.

Notes

Acknowledgements

A. Namburete is grateful for support from the Royal Academy of Engineering under the Engineering for Development Research Fellowship scheme. B. Papiez acknowledges Oxford NIHR Biomedical Research Centre (Rutherford Fund Fellowship at HDR UK). We thank the INTERGROWTH-21\(^\text {st}\) and INTERBIO-21\(^\text {st}\) Consortia for provision of 3D fetal US image data.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ana I. L. Namburete
    • 1
  • Raquel van Kampen
    • 1
  • Aris T. Papageorghiou
    • 2
  • Bartłomiej W. Papież
    • 1
    • 3
  1. 1.Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
  2. 2.Nuffield Department of Obstetrics and Gynaecology, John Radcliffe HospitalUniversity of OxfordOxfordUK
  3. 3.Big Data Institute, Li Ka Shing Centre for Health Information and DiscoveryUniversity of OxfordOxfordUK

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