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Magnetti, C.* ; Zimmer, V.* ; Ghavami, N.* ; Skelton, E.* ; Matthew, J.* ; Lloyd, K.* ; Hajnal, J.* ; Schnabel, J.A.* ; Gomez, A.*

Deep generative models to simulate 2D patient-specific ultrasound images in real time.

In: (Annual Conference on Medical Image Understanding and Analysis). Springer, 2020. 423-435 (Comm. Comp. Info. Sci. ; 1248 CCIS)
DOI
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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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Deep Learning ; Simulation ; Ultrasound
ISSN (print) / ISBN 1865-0929
e-ISSN 1865-0937
Konferenztitel Annual Conference on Medical Image Understanding and Analysis
Zeitschrift Communications in Computer and Information Science
Quellenangaben Band: 1248 CCIS, Heft: , Seiten: 423-435 Artikelnummer: , Supplement: ,
Verlag Springer
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)