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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
PublisherSPRINGER
Pages423-435
Number of pages13
ISBN (Print)9783030527907
DOIs
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

Conference

Conference24th Annual Conference on Medical Image Understanding and Analysis, MIUA 2020
Country/TerritoryUnited Kingdom
CityOxford
Period15/07/202017/07/2020

King's Authors

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 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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