Evaluation of an automatic intraluminal edge detection technique for intravascular ultrasound images.
Intravascular ultrasound (IVUS) imaging enables detailed analysis and precise measurements of vascular cross-sections. However, to achieve a reduction in the existing level of observer variability requires the development of quantitative IVUS. We have developed a fully automatic intraluminal edge detection technique, based on adaptive active contour models and called ADDER (adaptive damping dependent on echographic regions) that allows the quantitation of the intraluminal cross-sectional area (ICSA). Using a 30-MHz mechanically rotated transducer mounted at the tip of a 3.5-F catheter, 58 normal and pathologic arterial segments (from coronary, renal, splenic, iliac, and carotid arteries) were imaged in vitro. These images were analyzed by 2 experts, E1 and E2, who manually traced the intraluminal contour twice for each image, as well as with ADDER. Intra-observer variabilities for ICSAs were found to be excellent (-1.454 +/- 3.51% for E1, 0.96 +/- 5.4% for E2). The inter-observer variability was 2.1 +/- 4.3%. The success factor for ADDER was 89%. Its intra-observer variability was null, as the method always finds a unique contour. The correlation between the automatically detected ICSA and the manual ICSA was: r = 0.99 (y = 1.03x + 0.89 mm2). Morphometric variations between manually and automatically traced contours, analyzed by the centerline method, were 100 +/- 140 mm on average. In conclusion, the ADDER automatic contour detection applied to IVUS images is robust and characterized by small systematic and random errors; therefore, quantitative IVUS is a useful tool in clinical research trials.
Finet
G
G
Department of Hemodynamics and Radiology, Hospices Civils de Lyon, France.
Maurincomme
E
E
Reiber
J H
JH
Savalle
L
L
Magnin
I
I
Beaune
J
J
D001808:Blood Vessels / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D014463:Ultrasonography