1 research outputs found
Contrast Phase Classification with a Generative Adversarial Network
Dynamic contrast enhanced computed tomography (CT) is an imaging technique
that provides critical information on the relationship of vascular structure
and dynamics in the context of underlying anatomy. A key challenge for image
processing with contrast enhanced CT is that phase discrepancies are latent in
different tissues due to contrast protocols, vascular dynamics, and metabolism
variance. Previous studies with deep learning frameworks have been proposed for
classifying contrast enhancement with networks inspired by computer vision.
Here, we revisit the challenge in the context of whole abdomen contrast
enhanced CTs. To capture and compensate for the complex contrast changes, we
propose a novel discriminator in the form of a multi-domain disentangled
representation learning network. The goal of this network is to learn an
intermediate representation that separates contrast enhancement from anatomy
and enables classification of images with varying contrast time. Briefly, our
unpaired contrast disentangling GAN(CD-GAN) Discriminator follows the ResNet
architecture to classify a CT scan from different enhancement phases. To
evaluate the approach, we trained the enhancement phase classifier on 21060
slices from two clinical cohorts of 230 subjects. Testing was performed on 9100
slices from 30 independent subjects who had been imaged with CT scans from all
contrast phases. Performance was quantified in terms of the multi-class
normalized confusion matrix. The proposed network significantly improved
correspondence over baseline UNet, ResNet50 and StarGAN performance of accuracy
scores 0.54. 0.55, 0.62 and 0.91, respectively. The proposed discriminator from
the disentangled network presents a promising technique that may allow deeper
modeling of dynamic imaging against patient specific anatomies.Comment: 8 pages, 4 figure