AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases
Treatment: Status Quo
- URL: http://arxiv.org/abs/2108.02998v2
- Date: Mon, 3 Apr 2023 06:41:41 GMT
- Title: AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases
Treatment: Status Quo
- Authors: Yuan Jin, Antonio Pepe, Jianning Li, Christina Gsaxner, Fen-hua Zhao,
Kelsey L. Pomykala, Jens Kleesiek, Alejandro F. Frangi, Jan Egger
- Abstract summary: The aortic vessel tree is composed of the aorta and its branching arteries.
We systematically review computing techniques for the automatic and semi-automatic segmentation of the aortic vessel tree.
- Score: 55.04215695343928
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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