  Automated segmentation can assist radiotherapy treatment planning by saving
manual contouring efforts and reducing intra-observer and inter-observer
variations. The recent development of deep learning approaches has revoluted
medical data processing, including semantic segmentation, by dramatically
improving performance. However, training effective deep learning models usually
require a large amount of high-quality labeled data, which are often costly to
collect. We developed a novel semi-supervised adversarial deep learning
approach for 3D pelvic CT image semantic segmentation. Unlike supervised deep
learning methods, the new approach can utilize both annotated and un-annotated
data for training. It generates un-annotated synthetic data by a data
augmentation scheme using generative adversarial networks (GANs). We applied
the new approach to segmenting multiple organs in male pelvic CT images, where
CT images without annotations and GAN-synthesized un-annotated images were used
in semi-supervised learning. Experimental results, evaluated by three metrics
(Dice similarity coefficient, average Hausdorff distance, and average surface
Hausdorff distance), showed that the new method achieved either comparable
performance with substantially fewer annotated images or better performance
with the same amount of annotated data, outperforming the existing
state-of-the-art methods.
