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. 2022 Oct;57(4):357-363.
doi: 10.1053/j.ro.2022.07.001. Epub 2022 Jul 30.

Artificial Intelligence Applications in Aortic Dissection Imaging

Domenico Mastrodicasa  1 , Marina Codari  2 , Kathrin Bäumler  2 , Veit Sandfort  2 , Jody Shen  2 , Gabriel Mistelbauer  2 , Lewis D Hahn  3 , Valery L Turner  2 , Benoit Desjardins  4 , Martin J Willemink  2 , Dominik Fleischmann  2
Affiliations

Artificial Intelligence Applications in Aortic Dissection Imaging

Domenico Mastrodicasa et al. Semin Roentgenol. 2022 Oct.
No abstract available

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Conflict of interest statement

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: DM Activities related to the present article: research grant from the National Institute of Biomedical Imaging and Bioengineering (5T32EB009035). Activities not related to the present article: Shareholder of Segmed, Inc. Consulting for Segmed, Inc. Other relationships: no relevant relationships. MC Activities related to the present article: research grant from the American Heart Association (Award Number: 826389). Activities not related to the present article: none. Other relationships: none. MJW Activities related to the present article: research grant from the American Heart Association (18POST34030192). Activities not related to the present article: research grants from Philips Healthcare, and Stanford University, consulting for Arterys, Inc, and co-founder/shareholder of Segmed, Inc. Other relationships: no relevant relationships.

Figures

Figure 1
Figure 1
Overview of artificial intelligence applications in aortic dissection imaging.
Figure 2
Figure 2
Segmentation of multiplanar reformations obtained by using the fully automated pipeline. Segmentation was robust to the presence of false-lumen thrombosis (top), partial opacification of the false lumen (middle), and the presence of branch vessels (bottom) - Reprinted with permission from Hahn et al.
Figure 3
Figure 3
Visualization of the segmentation pipeline. Each arrow corresponds to a 3D residual U-Net. Model 1 segments all labels at once, whereas model 2 utilizes a cascade of networks. Model 3 is a single-step model that starts from the false lumen ground truth segmentation. CTA, computed tomography angiography - Reprinted with permission from Wobben et al.
See this image and copyright information in PMC

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