  Automated lesion segmentation from computed tomography (CT) is an important
and challenging task in medical image analysis. While many advancements have
been made, there is room for continued improvements. One hurdle is that CT
images can exhibit high noise and low contrast, particularly in lower dosages.
To address this, we focus on a preprocessing method for CT images that uses
stacked generative adversarial networks (SGAN) approach. The first GAN reduces
the noise in the CT image and the second GAN generates a higher resolution
image with enhanced boundaries and high contrast. To make up for the absence of
high quality CT images, we detail how to synthesize a large number of low- and
high-quality natural images and use transfer learning with progressively larger
amounts of CT images. We apply both the classic GrabCut method and the modern
holistically nested network (HNN) to lesion segmentation, testing whether SGAN
can yield improved lesion segmentation. Experimental results on the DeepLesion
dataset demonstrate that the SGAN enhancements alone can push GrabCut
performance over HNN trained on original images. We also demonstrate that HNN +
SGAN performs best compared against four other enhancement methods, including
when using only a single GAN.
