Automated segmentation of the aorta from phase contrast MR images: Validation against expert tracing in healthy volunteers and in patients with a dilated aorta - Sorbonne Université Accéder directement au contenu
Article Dans Une Revue Journal of Magnetic Resonance Imaging Année : 2010

Automated segmentation of the aorta from phase contrast MR images: Validation against expert tracing in healthy volunteers and in patients with a dilated aorta

Résumé

Purpose: To assess if segmentation of the aorta can be accurately achieved using the modulus image of phase contrast (PC) magnetic resonance (MR) acquisitions. Materials and methods: PC image sequences containing both the ascending and descending aorta of 52 subjects were acquired using three different MR scanners. An automated segmentation technique, based on a 2D+t deformable surface that takes into account the features of PC aortic images, such as flow-related effects, was developed. The study was designed to: 1) assess the variability of our approach and its robustness to the type of MR scanner, and 2) determine its sensitivity to aortic dilation and its accuracy against an expert manual tracing. Results: Interobserver variability in the lumen area was 0.59 +/- 0.92% for the automated approach versus 10.09 +/- 8.29% for manual segmentation. The mean Dice overlap measure was 0.945 +/- 0.014. The method was robust to the aortic size and highly correlated (r = 0.99) with the manual tracing in terms of aortic area and diameter. Conclusion: A fast and robust automated segmentation of the aortic lumen was developed and successfully tested on images provided by various MR scanners and acquired on healthy volunteers as well as on patients with a dilated aorta.

Dates et versions

hal-02642589 , version 1 (28-05-2020)

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Alain Herment, Nadjia Kachenoura, Muriel Lefort, Mourad Bensalah, Anas Dogui, et al.. Automated segmentation of the aorta from phase contrast MR images: Validation against expert tracing in healthy volunteers and in patients with a dilated aorta. Journal of Magnetic Resonance Imaging, 2010, 31 (4), pp.881-888. ⟨10.1002/jmri.22124⟩. ⟨hal-02642589⟩
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