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Munroe, L.* ; Sajith, G.* ; Lin, E.* ; Bhattacharya, S.* ; Pushparajah, K.* ; Simpson, J.A.* ; Schnabel, J.A.* ; Wheeler, G.* ; Gomez, A.* ; Deng, S.*

Automatic Re-orientation of 3D echocardiographic images in virtual reality using deep learning.

In: (Annual Conference on Medical Image Understanding and Analysis). Berlin [u.a.]: Springer, 2021. 177-188 (Lect. Notes Comput. Sc. ; 12722 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
In 3D echocardiography (3D echo), the image orientation varies depending on the position and direction of the transducer during examination. As a result, when reviewing images the user must initially identify anatomical landmarks to understand image orientation – a potentially challenging and time-consuming task. We automated this initial step by training a deep residual neural network (ResNet) to predict the rotation required to re-orient an image to the standard apical four-chamber view). Three data pre-processing strategies were explored: 2D, 2.5D and 3D. Three different loss function strategies were investigated: classification of discrete integer angles, regression with mean absolute angle error loss, and regression with geodesic loss. We then integrated the model into a virtual reality application and aligned the re-oriented 3D echo images with a standard anatomical heart model. The deep learning strategy with the highest accuracy – 2.5D classification of discrete integer angles – achieved a mean absolute angle error on the test set of 9.0∘. This work demonstrates the potential of artificial intelligence to support visualisation and interaction in virtual reality.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter 3d Echocardiography ; Deep Learning ; Virtual Reality
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Annual Conference on Medical Image Understanding and Analysis
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 12722 LNCS, Heft: , Seiten: 177-188 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)