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Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach



To develop and validate a deep learning–based algorithm for segmenting and quantifying the physiological and diseased aorta in computed tomography angiographies.


CTA exams of the aorta of 191 patients (68.1 ± 14 years, 128 male), performed between 2015 and 2018, were retrospectively identified from our imaging archive and manually segmented by two investigators. A 3D U-Net model was trained on the data, which was divided into a training, a validation, and a test group at a ratio of 7:1:2. Cases in the test group (n = 41) were evaluated to compare manual and automatic segmentations. Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff surface distance (HSD) were extracted. Maximum diameter, effective diameter, and area were quantified and compared between both segmentations at eight anatomical landmarks, and at the maximum area of an aneurysms if present (n = 14). Statistics included error calculation, intraclass correlation coefficient, and Bland-Altman analysis.


A DSC of 0.95 [0.94; 0.95] and an MSD of 0.76 [0.06; 0.99] indicated close agreement between segmentations. HSD was 8.00 [4.47; 10.00]. The largest absolute errors were found in the ascending aorta with 0.8 ± 1.5 mm for maximum diameter and at the coeliac trunk with − 30.0 ± 81.6 mm2 for area. Results for absolute errors in aneurysms were − 0.5 ± 2.3 mm for maximum diameter, 0.3 ± 1.6 mm for effective diameter, and 64.9 ± 114.9 mm2 for area. ICC showed excellent agreement (> 0.9; p < 0.05) between quantitative measurements.


Automated segmentation of the aorta on CTA data using a deep learning algorithm is feasible and allows for accurate quantification of the aortic lumen even if the vascular architecture is altered by disease.

Key Points

A deep learning–based algorithm can automatically segment the aorta, mostly within acceptable margins of error, even if the vascular architecture is altered by disease.

Quantifications performed in the segmentations were mostly within clinically acceptable limits, even in pathologically altered segments of the aorta.

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Convolutional neural networks


Dice similarity coefficient

Eff. diameter:

Effective vessel diameter


Hausdorff surface distance


Intraclass correlation coefficient

Max. diameter:

Maximum vessel diameter


Mean surface distance


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We deeply thank Dr. Karina Dietermann for her assistance in the statistical analysis.


The authors state that this work has not received any funding.

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Correspondence to Malte Maria Sieren.

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The scientific guarantor of this publication is Malte Maria Sieren.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Dr. Karina Dieterman kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was waived by the Institutional Review Board.

All patients were consented to the clinically indicated CT examination per clinical standard.

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Institutional Review Board approval was obtained.


• retrospective

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• performed at one institution

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Sieren, M.M., Widmann, C., Weiss, N. et al. Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach. Eur Radiol 32, 690–701 (2022).

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  • Artificial intelligence
  • Neuronal networks
  • Image processing
  • Computer-assisted, Aorta