An Automatic Method for Aortic Segmentation Based on Level-Set Methods Using Multiple Seed Points

Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 27)


Thoracic Aortic Aneurysm (TAA) is an enlargement of the aortic lumen at chest level. An accurate assessment of the geometry of the enlarged vessel is crucial when planning vascular interventions. This study developed an automatic method to extract aortic geometry and supra-aortic vessels from computerized tomography (CT) images. The proposed method consists of a fast-marching level-set method for detection of the initial aortic region from multiple seed points automatically selected along the pre-extracted vessel centerline, and a level-set method for extraction of the detailed aortic geometry from the initial aortic region. The automatic method was implemented inside Endosize (Therenva, Rennes), a commercially available software used for planning minimally invasive techniques. The performance of the algorithm was compare with the existing Endosize segmentation method (based on the region growing approach). For this comparison a CT dataset from an open source data file system (Osirix Advanced Imaging in 3D, 2016) was used. Results showed that, whilst the segmentation time increased (956 s for the new method, 0.308 s for the existing one), the new method produced a more accurate aortic segmentation, particularly in the region of supra-aortic branches. Further work to examine the efficacy of the proposed method should include a statistical study of performance across many datasets.


Thoracic Aortic Aneurysm (TAA) Automatic segmentation, Level-set method Virtual aortic surgery planning 



This work is funded by the European Commission through the H2020 Marie Sklodowska-Curie European VPH-CaSE Training Network (, GA No. 642612.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Mathematical Modelling in Medicine Group, IICD DepartmentUniversity of SheffieldSheffieldUK
  2. 2.Insigneo Institute for in Silico MedicineUniversity of SheffieldSheffieldUK
  3. 3.Therenva SASRennesFrance

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