Automated segmentation of the aorta from phase contrast MR images: validation against expert tracing in healthy volunteers and in patients with a dilated aorta.
OBJECTIVE
To assess if segmentation of the aorta can be accurately achieved using the modulus image of phase contrast (PC) magnetic resonance (MR) acquisitions.


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.


CONCLUSIONS
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.
Herment
Alain
A
Inserm U678, Paris, France. Alain.Herment@imed.jussieu.fr
Kachenoura
Nadjia
N
Lefort
Muriel
M
Bensalah
Mourad
M
Dogui
Anas
A
Frouin
Frédérique
F
Mousseaux
Elie
E
De Cesare
Alain
A
D001011:Aorta / Q000473:pathology* / Q000503:physiopathology; D001013:Aorta, Thoracic / Q000473:pathology; D001018:Aortic Diseases / Q000175:diagnosis* / Q000473:pathology*; D001331:Automation; D016022:Case-Control Studies; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D015233:Models, Statistical; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D012044:Regression Analysis; D015203:Reproducibility of Results