Deep Generative Models to Simulate 2D Patient-Specific Ultrasound Images in Real Time

Cesare Magnetti, Veronika Zimmer, Nooshin Ghavami, Emily Skelton, Jacqueline Matthew, Karen Lloyd, Jo Hajnal, Julia A. Schnabel, Alberto Gomez

Research output: Working paper/Preprint › Preprint

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Abstract

We present a computational method for real-time, patient-specific simulation of 2D ultrasound (US) images. The method uses a large number of tracked ultrasound images to learn a function that maps position and orientation of the transducer to ultrasound images. This is a first step towards realistic patient-specific simulations that will enable improved training and retrospective examination of complex cases. Our models can simulate a 2D image in under 4ms (well within real-time constraints), and produce simulated images that preserve the content (anatomical structures and artefacts) of real ultrasound images.
Original languageUndefined/Unknown
Publication statusPublished - 11 May 2020

Keywords

  • eess.IV
  • 94A08 (Primary) 68U10, 97R40 (Secondary)

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    • 1 Conference paper

    • Deep Generative Models to Simulate 2D Patient-Specific Ultrasound Images in Real Time

      Magnetti, C., Zimmer, V., Ghavami, N., Skelton, E., Matthew, J., Lloyd, K., Hajnal, J., Schnabel, J. A. & Gomez, A., 1 Jan 2020, Medical Image Understanding and Analysis - 24th Annual Conference, MIUA 2020, Proceedings. Papiez, B. W., Namburete, A. I. L., Yaqub, M., Noble, J. A. & Yaqub, M. (eds.). SPRINGER, p. 423-435 13 p. (Communications in Computer and Information Science; vol. 1248 CCIS).

      Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

      1 Citation (Scopus)

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