Type B Aortic Dissection CTA Collection with True and False Lumen Expert Annotations for the Development of AI-based Algorithms.


Journal

Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192

Informations de publication

Date de publication:
06 Jun 2024
Historique:
received: 15 05 2023
accepted: 22 04 2024
medline: 7 6 2024
pubmed: 7 6 2024
entrez: 6 6 2024
Statut: epublish

Résumé

Aortic dissections (ADs) are serious conditions of the main artery of the human body, where a tear in the inner layer of the aortic wall leads to the formation of a new blood flow channel, named false lumen. ADs affecting the aorta distally to the left subclavian artery are classified as a Stanford type B aortic dissection (type B AD). This is linked to substantial morbidity and mortality, however, the course of the disease for the individual case is often unpredictable. Computed tomography angiography (CTA) is the gold standard for the diagnosis of type B AD. To advance the tools available for the analysis of CTA scans, we provide a CTA collection of 40 type B AD cases from clinical routine with corresponding expert segmentations of the true and false lumina. Segmented CTA scans might aid clinicians in decision making, especially if it is possible to fully automate the process. Therefore, the data collection is meant to be used to develop, train and test algorithms.

Identifiants

pubmed: 38844767
doi: 10.1038/s41597-024-03284-2
pii: 10.1038/s41597-024-03284-2
doi:

Types de publication

Dataset Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

596

Subventions

Organisme : Technische Universität Graz (Graz University of Technology)
ID : LEAD Project on Aortic Dissection

Informations de copyright

© 2024. The Author(s).

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Auteurs

Christian Mayer (C)

Division of Cardiac Surgery, Department of Surgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria.

Antonio Pepe (A)

Institute of Computer Graphics and Vision (ICG), Graz University of Technology, Inffeldgasse 16/II, 8010, Graz, Austria.

Sophie Hossain (S)

Division of Cardiac Surgery, Department of Surgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria.

Barbara Karner (B)

Division of Cardiac Surgery, Department of Surgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria.

Melanie Arnreiter (M)

Division of Cardiac Surgery, Department of Surgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria.

Jens Kleesiek (J)

Institute for Artificial Intelligence in Medicine (IKIM), AI-guided Therapies (AIT), Essen University Hospital (AöR), Girardetstraße 2, 45131, Essen, Germany.

Johannes Schmid (J)

Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036, Graz, Austria.

Michael Janisch (M)

Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036, Graz, Austria.

Deutschmann Hannes (D)

Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036, Graz, Austria.

Michael Fuchsjäger (M)

Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036, Graz, Austria.

Daniel Zimpfer (D)

Division of Cardiac Surgery, Department of Surgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria.

Jan Egger (J)

Institute of Computer Graphics and Vision (ICG), Graz University of Technology, Inffeldgasse 16/II, 8010, Graz, Austria. jan.egger@uk-essen.de.
Institute for Artificial Intelligence in Medicine (IKIM), AI-guided Therapies (AIT), Essen University Hospital (AöR), Girardetstraße 2, 45131, Essen, Germany. jan.egger@uk-essen.de.

Heinrich Mächler (H)

Division of Cardiac Surgery, Department of Surgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria. heinrich.maechler@medunigraz.at.

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