Detection, segmentation, simulation and visualization of aortic dissections: A review

Antonio Pepe*, Jianning Li*, Malte Rolf-Pissarczyk, Christina Schwarz-Gsaxner, Xiaojun Chen, Gerhard Holzapfel, Jan Egger*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


Aortic dissection (AD) is a condition of the main artery of the human body, resulting in the formation of a new flow channel, or false lumen. The disease is usually diagnosed with a computed tomography angiography scan during the acute phase. A better understanding of the causes of AD requires knowledge of the aortic geometry (segmentation), including the true and false lumina, which is very time-consuming to reconstruct when performed manually on a slice-by-slice basis. Hence, different automatic and semi-automatic medical image analysis approaches have been proposed for this task over the last years. In this review, we present and discuss these computing techniques used to segment dissected aortas, also in regard to the detection and visualization of clinically relevant information and features from dissected aortas for customized patient-specific treatments.

Original languageEnglish
Article number101773
Number of pages16
JournalMedical Image Analysis
Publication statusPublished - 2020


  • Aorta
  • Computed tomography
  • Detection
  • Dissection
  • Segmentation
  • Simulation
  • Visualization

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Health Informatics
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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