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Automatic Shadow Detection in 2D Ultrasound Images

  • Qingjie Meng
  • Christian Baumgartner
  • Matthew Sinclair
  • James Housden
  • Martin Rajchl
  • Alberto Gomez
  • Benjamin Hou
  • Nicolas Toussaint
  • Veronika Zimmer
  • Jeremy Tan
  • Jacqueline Matthew
  • Daniel Rueckert
  • Julia Schnabel
  • Bernhard Kainz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11076)

Abstract

Automatically detecting acoustic shadows is of great importance for automatic 2D ultrasound analysis ranging from anatomy segmentation to landmark detection. However, variation in shape and similarity in intensity to other structures make shadow detection a very challenging task. In this paper, we propose an automatic shadow detection method to generate a pixel-wise, shadow-focused confidence map from weakly labelled, anatomically-focused images. Our method: (1) initializes potential shadow areas based on a classification task. (2) extends potential shadow areas using a GAN model. (3) adds intensity information to generate the final confidence map using a distance matrix. The proposed method accurately highlights the shadow areas in 2D ultrasound datasets comprising standard view planes as acquired during fetal screening. Moreover, the proposed method outperforms the state-of-the-art quantitatively and improves failure cases for automatic biometric measurement.

Notes

Acknowledgments

Supported by the Wellcome Trust IEH Award [102431] and Nvidia Corporation.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Qingjie Meng
    • 1
  • Christian Baumgartner
    • 2
  • Matthew Sinclair
    • 1
  • James Housden
    • 3
  • Martin Rajchl
    • 1
  • Alberto Gomez
    • 3
  • Benjamin Hou
    • 1
  • Nicolas Toussaint
    • 3
  • Veronika Zimmer
    • 3
  • Jeremy Tan
    • 1
  • Jacqueline Matthew
    • 3
  • Daniel Rueckert
    • 1
  • Julia Schnabel
    • 3
  • Bernhard Kainz
    • 1
  1. 1.Biomedical Image Analysis GroupImperial College LondonLondonUK
  2. 2.Computer Vision LabETH ZürichZürichSwitzerland
  3. 3.School of Biomedical Engineering and Imaging SciencesKings College LondonLondonUK

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