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With the development of hybrid imaging scanners, micro-CT is widely used
in locating abnormalities, studying drug metabolism, and providing
structural priors to aid image reconstruction in functional imaging. Due
to the low contrast of soft tissues, segmentation of soft tissue organs
from mouse micro-CT images is a challenging problem. In this paper, we
propose a mouse segmentation scheme based on dynamic contrast enhanced
micro-CT images. With a homemade fast scanning micro-CT scanner, dynamic
contrast enhanced images were acquired before and after injection of
non-ionic iodinated contrast agents (iohexol). Then the feature vector of
each voxel was extracted from the signal intensities at different time
points. Based on these features, the heart, liver, spleen, lung, and
kidney could be classified into different categories and extracted from
separate categories by morphological processing. The bone structure was
segmented using a thresholding method. Our method was validated on seven
BALB/c mice using two different classifiers: a support vector machine
classifier with a radial basis function kernel and a random forest
classifier. The results were compared to manual segmentation, and the
performance was assessed using the Dice similarity coefficient, false
positive ratio, and false negative ratio. The results showed high accuracy
with the Dice similarity coefficient ranging from 0.709 ± 0.078 for the
spleen to 0.929 ± 0.006 for the kidney.
481 views reported since publication in 2017.