  Purpose: To apply a convolutional neural network (CNN) to develop a system
that segments intensity calibration phantom regions in computed tomography (CT)
images, and to test the system in a large cohort to evaluate its robustness.
Methods: A total of 1040 cases (520 cases each from two institutions), in which
an intensity calibration phantom (B-MAS200, Kyoto Kagaku, Kyoto, Japan) was
used, were included herein. A training dataset was created by manually
segmenting the regions of the phantom for 40 cases (20 cases each).
Segmentation accuracy of the CNN model was assessed with the Dice coefficient
and the average symmetric surface distance (ASD) through the 4-fold cross
validation. Further, absolute differences of radiodensity values (in Hounsfield
units: HU) were compared between manually segmented regions and automatically
segmented regions. The system was tested on the remaining 1000 cases. For each
institution, linear regression was applied to calculate coefficients for the
correlation between radiodensity and the densities of the phantom. Results:
After training, the median Dice coefficient was 0.977, and the median ASD was
0.116 mm. When segmented regions were compared between manual segmentation and
automated segmentation, the median absolute difference was 0.114 HU. For the
test cases, the median correlation coefficient was 0.9998 for one institution
and was 0.9999 for the other, with a minimum value of 0.9863. Conclusions: The
CNN model successfully segmented the calibration phantom's regions in the CT
images with excellent accuracy, and the automated method was found to be at
least equivalent to the conventional manual method. Future study should
integrate the system by automatically segmenting the region of interest in
bones such that the bone mineral density can be fully automatically quantified
from CT images.
