AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks

• Published in: Data in brief Vol. 40; p. 107801
• Main Authors: Radl, Lukas, Jin, Yuan, Pepe, Antonio, Li, Jianning, Gsaxner, Christina, Zhao, Fen-hua, Egger, Jan
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.02.2022; Elsevier
• Subjects: Abdominal aortic aneurysm; angiography; Aorta; Aortic dissection; computed tomography; CTA; Data; data collection; Deep learning; geometry; Ground truth; humans; Masks; neck; Segmentations; statistical models; trees; Vessel tree; Deep learning; CTA; Vessel tree; Abdominal aortic aneurysm; Aorta; Aortic dissection; Segmentations; Ground truth; Masks
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2022.107801

In this article, we present a multicenter aortic vessel tree database collection, containing 56 aortas and their branches. The datasets have been acquired with computed tomography angiography (CTA) scans and each scan covers the ascending aorta, the aortic arch and its branches into the head/neck area, the thoracic aorta, the abdominal aorta and the lower abdominal aorta with the iliac arteries branching into the legs. For each scan, the collection provides a semi-automatically generated segmentation mask of the aortic vessel tree (ground truth). The scans come from three different collections and various hospitals, having various resolutions, which enables studying the geometry/shape variabilities of human aortas and its branches from different geographic locations. Furthermore, creating a robust statistical model of the shape of human aortic vessel trees, which can be used for various tasks such as the development of fully-automatic segmentation algorithms for new, unseen aortic vessel tree cases, e.g. by training deep learning-based approaches. Hence, the collection can serve as an evaluation set for automatic aortic vessel tree segmentation algorithms.