Pepe A, Li J, Rolf-Pissarczyk M, Gsaxner C, Chen X, Holzapfel GA, Egger J
Publication Type: Journal article, Review article
Book Volume: 65
Article Number: 101773
DOI: 10.1016/j.media.2020.101773
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. The disease is usually diagnosed with a computed tomography angiography scan during the acute phase. A better understanding of the causes of AD requires knowledge of the aortic geometry (segmentation), including the true and false lumina, which is very time-consuming to reconstruct when performed manually on a slice-by-slice basis. Hence, different automatic and semi-automatic medical image analysis approaches have been proposed for this task over the last years. In this review, we present and discuss these computing techniques used to segment dissected aortas, also in regard to the detection and visualization of clinically relevant information and features from dissected aortas for customized patient-specific treatments.
APA:
Pepe, A., Li, J., Rolf-Pissarczyk, M., Gsaxner, C., Chen, X., Holzapfel, G.A., & Egger, J. (2020). Detection, segmentation, simulation and visualization of aortic dissections: A review. Medical Image Analysis, 65. https://doi.org/10.1016/j.media.2020.101773
MLA:
Pepe, Antonio, et al. "Detection, segmentation, simulation and visualization of aortic dissections: A review." Medical Image Analysis 65 (2020).
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