4 research outputs found

    Aortic Dissection Dataset and Segmentations

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    This dataset consists of a type B Aortic Dissection (AD) CTA collection with true and false lumen expert annotations. It can be used for the development of AI-based algorithms for an automatic segmentation of ADs.For a review about the detection, segmentation, simulation and visualization of Aortic Dissections, please see:Pepe A, Li J, Rolf-Pissarczyk M, Gsaxner C, Chen X, Holzapfel GA, Egger J. Detection, segmentation, simulation and visualization of aortic dissections: A review. Medical image analysis. 2020 Oct 1;65:101773.Cite as:[1] Christian Mayer, Antonio Pepe, Sophie Hossain, Barbara Karner, Melanie Arnreiter, Jens Kleesiek, Johannes Schmid, Michael Janisch, Hannes Deutschmann, Michael Fuchsjäger, Daniel Zimpfer, Jan Egger, Heinrich Mächler. Aortic Dissection Dataset and Segmentations. figshare, 2024. DOI: 10.6084/m9.figshare.22269091[2] Christian Mayer, Antonio Pepe, Sophie Hossain, Barbara Karner, Melanie Arnreiter, Jens Kleesiek, Johannes Schmid, Michael Janisch, Hannes Deutschmann, Michael Fuchsjäger, Daniel Zimpfer, Jan Egger, Heinrich Mächler. Type B Aortic Dissection CTA Collection with True and False Lumen Expert Annotations for the Development of AI-based Algorithms. Scientific Data, Nature Portfolio 2024.</p

    Demographic data.

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    <p>Note: Results from <i>t</i>-tests (<i>t</i>), Chi square tests (<i>χ2</i>), Mann-Whitney-U-tests (<i>U</i>) and ANCOVA (<i>F</i>)</p><p>(1) For ANCOVA, WML volumes were normalized by individual total intracranial volume and controlled for age, diabetes, smoking and BMI.</p><p>Statistically significant effects are marked bold.</p><p>Demographic data.</p
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