Readings in Vision and Graphics
edited by Luc Van Gool, Gábor Székely, Markus Gross, Bernt Schiele
Automatic Segmentation of the Vessel Lumen
from 3D CTA Images of Aortic Dissection.
2010. X, 122 pages. EUR 64,00. ISBN-10: 3-86628-359-8
Acute aortic dissection is a cardiovascular emergency and can quickly lead to death, therefore it requires prompt diagnosis and treatment. The reliable identification of the true and false lumen is crucial for treatment planning. This is, however, a difficult and time-consuming task, even for trained professionals. A computer aided support system capable of displaying the different lumina in an easily comprehensible way is still not available.
This book, therefore, aims at providing the radiologist a fully automatic computer aided diagnosing system, which is able to identify and visualize the dissection lumina. It is believed that this generic tool significantly helps the diagnosis of abdominal aortic dissection and contributes to a faster treatment planning and a more grounded decision-making.
Firstly, the segmentation of the entire aorta including the aortic arch as well as the ascending- and descending sections is presented. Thereafter, approaches for detection of the dissection membrane and identification of the true and false lumen are discussed. The proposed method is robust against acquisition artifacts typically present in 3D CTA images of aortic dissections, it has a clinically acceptable accuracy and it is significantly faster than manual segmentation.
Abotu the Author:
Tamás Kovács obtained his M.Sc. degree in Electrical Engineering from the Technical University Budapest, Hungary, in 2000. From 2003 to 2007, he was research assistant and Ph.D. student at the Computer Vision Laboratory of ETH Zürich, Switzerland. In 2010, he finished his doctoral thesis entitled "Automatic Segmentation of the Vessel Lumen from 3D CTA Images of Aortic Dissection" and was awarded a Ph.D. degree (Dr. sc. ETH Zürich) from ETH Zürich.
Keywords: Aorta Segmentation, Lumen Identification, Membrane Detection, Aortic Dissection, 3D CTA, Computer Aided Diagnosis.
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