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Status: Bibliographieeintrag

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Verfasst von:Andlauer, Robin [VerfasserIn]   i
 Wachter, Andreas [VerfasserIn]   i
 Schaufelberger, Matthias [VerfasserIn]   i
 Bouffleur, Frederic [VerfasserIn]   i
 Kühle, Reinald [VerfasserIn]   i
 Freudlsperger, Christian [VerfasserIn]   i
 Nahm, Werner [VerfasserIn]   i
Titel:3D-guided face manipulation of 2D images for the prediction of post-operative outcome after cranio-maxillofacial surgery
Verf.angabe:Robin Andlauer, Andreas Wachter, Matthias Schaufelberger, Frederic Weichel, Reinald Kühle, Christian Freudlsperger, and Werner Nahm
E-Jahr:2021
Jahr:July 15, 2021
Umfang:15 S.
Fussnoten:Gesehen am 07.10.2021
Titel Quelle:Enthalten in: Institute of Electrical and Electronics EngineersIEEE transactions on image processing
Ort Quelle:New York, NY : IEEE, 1992
Jahr Quelle:2021
Band/Heft Quelle:30(2021), Seite 7349-7363
ISSN Quelle:1941-0042
Abstract:Cranio-maxillofacial surgery often alters the aesthetics of the face which can be a heavy burden for patients to decide whether or not to undergo surgery. Today, physicians can predict the post-operative face using surgery planning tools to support the patient's decision-making. While these planning tools allow a simulation of the post-operative face, the facial texture must usually be captured by another 3D texture scan and subsequently mapped on the simulated face. This approach often results in face predictions that do not appear realistic or lively looking and are therefore ill-suited to guide the patient's decision-making. Instead, we propose a method using a generative adversarial network to modify a facial image according to a 3D soft-tissue estimation of the post-operative face. To circumvent the lack of available data pairs between pre- and post-operative measurements we propose a semi-supervised training strategy using cycle losses that only requires paired open-source data of images and 3D surfaces of the face's shape. After training on "in-the-wild" images we show that our model can realistically manipulate local regions of a face in a 2D image based on a modified 3D shape. We then test our model on four clinical examples where we predict the post-operative face according to a 3D soft-tissue prediction of surgery outcome, which was simulated by a surgery planning tool. As a result, we aim to demonstrate the potential of our approach to predict realistic post-operative images of faces without the need of paired clinical data, physical models, or 3D texture scans.
DOI:doi:10.1109/TIP.2021.3096081
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1109/TIP.2021.3096081
 Volltext: https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=DynamicDOIArticle&SrcApp=WOS&KeyAID=10.1109%2 ...
 DOI: https://doi.org/10.1109/TIP.2021.3096081
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:3D morphable model
 Cranio-maxillofacial surgery
 cycle loss
 CycleGAN
 face editing
 face manipulation
 Faces
 gan
 generative adversarial network
 Planning
 post-operative face
 Predictive models
 Solid modeling
 Surgery
 surgery planning
 Three-dimensional displays
 Tools
 unsupervised learning
K10plus-PPN:1772749699
Verknüpfungen:→ Zeitschrift

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