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• W3187580752 abstract "The aortic vessel tree is composed of the aorta and its branching arteries, and plays a key role in supplying the whole body with blood. Aortic diseases, like aneurysms or dissections, can lead to an aortic rupture, whose treatment with open surgery is highly risky. Therefore, patients commonly undergo drug treatment under constant monitoring, which requires regular inspections of the vessels through imaging. The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if completed with a contrast agent, called CT angiography (CTA). Optimally, the whole aortic vessel tree geometry from consecutive CTAs is overlaid and compared. This allows not only detection of changes in the aorta, but also of its branches, caused by the primary pathology or newly developed. When performed manually, this reconstruction requires slice by slice contouring, which could easily take a whole day for a single aortic vessel tree, and is therefore not feasible in clinical practice. Automatic or semi-automatic vessel tree segmentation algorithms, however, can complete this task in a fraction of the manual execution time and run in parallel to the clinical routine of the clinicians. In this paper, we systematically review computing techniques for the automatic and semi-automatic segmentation of the aortic vessel tree. The review concludes with an in-depth discussion on how close these state-of-the-art approaches are to an application in clinical practice and how active this research field is, taking into account the number of publications, datasets and challenges." @default.
• W3187580752 created "2021-08-16" @default.
• W3187580752 creator A5002069152 @default.
• W3187580752 creator A5010662766 @default.
• W3187580752 creator A5017161970 @default.
• W3187580752 creator A5025446671 @default.
• W3187580752 creator A5033911788 @default.
• W3187580752 creator A5035986375 @default.
• W3187580752 creator A5042171930 @default.
• W3187580752 creator A5065229483 @default.
• W3187580752 date "2021-08-06" @default.
• W3187580752 modified "2023-09-23" @default.
• W3187580752 title "AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo" @default.
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• W3187580752 doi "https://doi.org/10.48550/arxiv.2108.02998" @default.

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