Automatic lumen and outer wall segmentation of the carotid artery using deformable three-dimensional models in MR angiography and vessel wall images.
OBJECTIVE
To develop and validate an automated segmentation technique for the detection of the lumen and outer wall boundaries in MR vessel wall studies of the common carotid artery.


METHODS
A new segmentation method was developed using a three-dimensional (3D) deformable vessel model requiring only one single user interaction by combining 3D MR angiography (MRA) and 2D vessel wall images. This vessel model is a 3D cylindrical Non-Uniform Rational B-Spline (NURBS) surface which can be deformed to fit the underlying image data. Image data of 45 subjects was used to validate the method by comparing manual and automatic segmentations. Vessel wall thickness and volume measurements obtained by both methods were compared.


RESULTS
Substantial agreement was observed between manual and automatic segmentation; over 85% of the vessel wall contours were segmented successfully. The interclass correlation was 0.690 for the vessel wall thickness and 0.793 for the vessel wall volume. Compared with manual image analysis, the automated method demonstrated improved interobserver agreement and inter-scan reproducibility. Additionally, the proposed automated image analysis approach was substantially faster.


CONCLUSIONS
This new automated method can reduce analysis time and enhance reproducibility of the quantification of vessel wall dimensions in clinical studies.
Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.
Ronald
R
van 't Klooster
Patrick J H
PJ
de Koning
Reza Alizadeh
RA
Dehnavi
Jouke T
JT
Tamsma
Albert
A
de Roos
Johan H C
JH
Reiber
Rob J
RJ
van der Geest
D000328:Adult; D000368:Aged; D001158:Arteries / Q000473:pathology; D050197:Atherosclerosis / Q000175:diagnosis / Q000473:pathology; D002339:Carotid Arteries / Q000473:pathology; D017536:Carotid Artery, Common / Q000473:pathology*; D001330:Electronic Data Processing; D004867:Equipment Design; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D018810:Magnetic Resonance Angiography / Q000295:instrumentation / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015588:Observer Variation