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

Area of Science:

• Cardiovascular Imaging
• Medical Informatics
• Radiology

Background:

• Aortic dissections (ADs) are life-threatening conditions involving a tear in the aorta's inner layer, creating a false lumen.
• Stanford type B aortic dissection (type B AD) specifically affects the aorta distal to the left subclavian artery, posing significant morbidity and mortality risks.
• The unpredictable nature of type B AD necessitates advanced diagnostic and analytical tools.

Purpose of the Study:

• To present a novel dataset of computed tomography angiography (CTA) scans for type B AD.
• To provide expert segmentations of true and false lumina for 40 clinical cases.
• To facilitate the development, training, and testing of automated algorithms for analyzing type B AD CTA scans.

Main Methods:

• Compilation of a dataset comprising 40 type B AD cases from routine clinical practice.
• Expert-driven segmentation of true and false lumina on CTA scans.
• Data collection designed for algorithmic development and validation.

Main Results:

• A comprehensive dataset of 40 segmented CTA scans for type B AD is now available.
• The dataset includes detailed segmentations of the true and false lumina, crucial for analysis.
• This resource supports the advancement of automated analysis tools for type B AD.

Conclusions:

• The presented CTA dataset is a valuable resource for advancing research in type B aortic dissection.
• Automated analysis of segmented CTA scans holds potential for improved clinical decision-making.
• This data collection will accelerate the development of AI-driven tools for diagnosing and managing type B AD.