Characterization of small nodules by automatic segmentation of X-ray computed tomography images.
OBJECTIVE
To characterize the ability of an automatic lung nodule segmentation algorithm to measure small nodule dimensions and growth rates.


METHODS
A phantom of 20 sets of 6 balls each (11 different nylon balls and 9 acrylic balls) of 1 to 9.5 mm in diameter, in foam, was imaged using x-ray computed tomography with slice thicknesses of 5, 2.5, and 1.25 mm, pitches of 3 and 6, and standard and lung resolution. Measurements consisted of volume and maximum in-plane cross-sectional areas and their derived maximum and effective diameters. Growth rates were simulated using pairs of groups of balls.


RESULTS
Volume measurements overestimate volume, more so for thicker slices. For the largest balls, the error is 60% for 5-mm slices and 20% for 1.25-mm slices. Effective diameter calculated from volume better approximates actual diameter. For area measurements, errors are 0% to 5% for the largest balls, and the effective and actual diameters are closely matched.


CONCLUSIONS
Below 5 mm in diameter, changes in volume should reach 100% for reliable indication of growth. Above 6 mm, the threshold for detecting change is on the order of 25% growth. Even under ideal conditions, results indicate the need for caution when making a diagnosis of malignancy on the basis of volume change.
Tao
Peng
P
AccuImage Diagnostics Corporation, South San Francisco, CA, USA.
Griess
Friederike
F
Lvov
Yelena
Y
Mineyev
Mikhail
M
Zhao
Binsheng
B
Levin
David
D
Kaufman
Leon
L
D000465:Algorithms; D021621:Imaging, Three-Dimensional; D008171:Lung Diseases / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*