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UltraGAN: Ultrasound Enhancement Through Adversarial Generation

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Simulation and Synthesis in Medical Imaging (SASHIMI 2020)

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Abstract

Ultrasound images are used for a wide variety of medical purposes because of their capacity to study moving structures in real time. However, the quality of ultrasound images is significantly affected by external factors limiting interpretability. We present UltraGAN, a novel method for ultrasound enhancement that transfers quality details while preserving structural information. UltraGAN incorporates frequency loss functions and an anatomical coherence constraint to perform quality enhancement. We show improvement in image quality without sacrificing anatomical consistency. We validate UltraGAN on a publicly available dataset for echocardiography segmentation and demonstrate that our quality-enhanced images are able to improve downstream tasks. To ensure reproducibility we provide our source code and training models.

M. Escobar and A. Castillo—Both authors contributed equally to this work.

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Notes

  1. 1.

    https://github.com/BCV-Uniandes/UltraGAN.

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Acknowledgements

The present study is funded by MinCiencias, contract number 853-2019 project ID# 120484267276.

Author information

Authors and Affiliations

  1. Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, Colombia

    Maria Escobar, Angela Castillo & Pablo Arbeláez

  2. Computer Vision Lab, ETHZ, Zürich, Switzerland

    Andrés Romero

Authors
  1. Maria Escobar
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  2. Angela Castillo
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  3. Andrés Romero
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  4. Pablo Arbeláez
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Corresponding author

Correspondence to Maria Escobar .

Editor information

Editors and Affiliations

  1. CNRS – Paris Brain Institute, Paris, France

    Ninon Burgos

  2. Masaryk University, Brno, Czech Republic

    David Svoboda

  3. University of Twente, Enschede, The Netherlands

    Jelmer M. Wolterink

  4. Johns Hopkins University, Bethesda, MD, USA

    Can Zhao

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Escobar, M., Castillo, A., Romero, A., Arbeláez, P. (2020). UltraGAN: Ultrasound Enhancement Through Adversarial Generation. In: Burgos, N., Svoboda, D., Wolterink, J.M., Zhao, C. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2020. Lecture Notes in Computer Science(), vol 12417. Springer, Cham. https://doi.org/10.1007/978-3-030-59520-3_13

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  • DOI: https://doi.org/10.1007/978-3-030-59520-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59519-7

  • Online ISBN: 978-3-030-59520-3

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