OA-GAN: Organ-Aware Generative Adversarial Network for Synthesizing Contrast-enhanced Medical Images.

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Abstract

Contrast-enhanced computed tomography (CE-CT) images are vital
for clinical diagnosis of focal liver lesions (FLLs). However, the use of CE-CT
images imposes a significant burden on patients due to the injection of contrast
agents and extended shooting. Deep learning-based image synthesis models offer
a promising solution that synthesizes CE-CT images from non-contrasted CT
(NC-CT) images. Unlike natural images, medical image synthesis requires a
specific focus on certain organs or localized regions to ensure accurate diagnosis.
Determining how to effectively emphasize target organs poses a challenging issue
in medical image synthesis. To solve this challenge, we present a novel CECT image synthesis model called, Organ-Aware Generative Adversarial Network
(OA-GAN). The OA-GAN comprises an organ-aware (OA) network and a dual
decoder-based generator. First, the OA network learns the most discriminative
spatial features about the target organ (i.e., liver) by utilizing the ground truth
organ mask as localization cues. Subsequently, NC-CT image and captured
feature are fed into the dual decoder-based generator, which employs a local and
global decoder network to simultaneously synthesize the organ and entire CECT
image. Moreover, the semantic information extracted from the local decoder is
transferred to the global decoder to facilitate better reconstruction of the organ
in entire CE-CT image. The qualitative and quantitative evaluation on a CE-CT
dataset demonstrates that the OA-GAN outperforms state-of-the-art approaches
for synthesizing two types of CE-CT images such as arterial phase and portal
venous phase. Additionally, subjective evaluations by expert radiologists and a
deep learning-based FLLs classification also affirm that CE-CT images synthesized
from the OA-GAN exhibit a remarkable resemblance to real CE-CT images.© 2024 IOP Publishing Ltd.