Abstract
Aortic dissection (AD) is a condition of the main artery of the human body, resulting in the formation of a new flow channel, or false lumen (FL). The disease is usually diagnosed with a computed tomography angiography (CTA) scan during the acute phase. A better understanding of the causes of AD requires knowledge of aortic geometry prior to the event, which is available only in very rare circumstances. In this work, we propose an approach to reconstruct the aorta before the formation of a dissection by performing 3D inpainting with a two-stage generative adversarial network (GAN). In the first stage of our two-stage GAN, a network is trained on the 3D edge information of the healthy aorta to reconstruct the aortic wall. The second stage infers the image information of the aorta to reconstruct the entire dataset. We train our two-stage GAN with 3D patches from 55 non-dissected aortic datasets and evaluate it on 20 more non-dissected datasets, demonstrating that our proposed 3D architecture outperforms its 2D counterpart. To obtain pre-dissection aortae, we mask the entire FL in AD datasets. Finally, we provide qualitative feedback from a renown expert on the obtained pre-dissection cases.
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Notes
- 1.
Deep Learning Inpainting of Aortic Dissections with Studierfenster: https://www.youtube.com/watch?v=c85qV-CDOX4.
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Acknowledgement
This work received fundings from the TU Graz LEAD Project Mechanics, Modeling and Simulation of Aortic Dissection (https://www.tugraz.at/projekte/biomechaorta/BioMechAorta), the Austrian Science Fund (FWF) KLI 678-B31 enFaced and the COMET K-Project 871132 CAMed of the Austrian Research Promotion Agency (FFG). Antonio Pepe was also supported by an Austrian Marshall Plan Foundation Scholarship (Scholarship n. 942121022222019)Â [18]. Additionally, the authors would like to thank Professor Dr. Gerhard A. Holzapfel (Graz University of Technology) for his advice.
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Pepe, A. et al. (2020). Semi-supervised Virtual Regression of Aortic Dissections Using 3D Generative Inpainting. In: Petersen, J., et al. Thoracic Image Analysis. TIA 2020. Lecture Notes in Computer Science(), vol 12502. Springer, Cham. https://doi.org/10.1007/978-3-030-62469-9_12
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DOI: https://doi.org/10.1007/978-3-030-62469-9_12
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