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

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis - 24th Annual Conference, MIUA 2020, Proceedings
EditorsBartlomiej W. Papiez, Ana I.L. Namburete, Mohammad Yaqub, J. Alison Noble, Mohammad Yaqub
Number of pages13
ISBN (Print)9783030527907
Published1 Jan 2020
Event24th Annual Conference on Medical Image Understanding and Analysis, MIUA 2020 - Oxford, United Kingdom
Duration: 15 Jul 202017 Jul 2020

Publication series

NameCommunications in Computer and Information Science
Volume1248 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference24th Annual Conference on Medical Image Understanding and Analysis, MIUA 2020
Country/TerritoryUnited Kingdom

King's Authors


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 4 ms (well within real-time constraints), and produce simulated images that preserve the content (anatomical structures and artefacts) of real ultrasound images.

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