Semi-supervised Virtual Regression of Aortic Dissections Using 3D Generative Inpainting-Reference-Cited by-同舟云学术

Semi-supervised Virtual Regression of Aortic Dissections Using 3D Generative Inpainting

Published:2020 Issue: Volume: Page:130-140
ISSN:0302-9743
Container-title:Thoracic Image Analysis
language:
Short-container-title:

Author:

Pepe Antonio^ORCID,Mistelbauer Gabriel^ORCID,Gsaxner Christina^ORCID,Li Jianning^ORCID,Fleischmann Dominik^ORCID,Schmalstieg Dieter^ORCID,Egger Jan^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-62469-9_12

Reference26 articles.

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3. Bäumler, K., Vedula, V., Sailer, A.M., et al.: Fluid-structure interaction simulations of patient-specific aortic dissection. Biomech. Model. Mechanobiol. (2020). https://doi.org/10.1007/s10237-020-01294-8

4. Daily, P.O., Trueblood, H.W., Stinson, E.B., et al.: Management of acute aortic dissections. Ann. Thoracic Surg. 10(3), 237–247 (1970). https://doi.org/10.1016/S0003-4975(10)65594-4

5. Egger, J., Gunacker, S., Pepe, A., et al.: A comprehensive workflow and framework for immersive virtual endoscopy of dissected aortae from CTA data. In: SPIE Medical Imaging 1131531 (2020). https://doi.org/10.1117/12.2559239

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