Lung image segmentation by generative adversarial networks
This addresses lung image segmentation for medical diagnosis, but it appears incremental as it applies existing GAN techniques to a specific domain without clear novel methodological contributions.
The paper tackled lung image segmentation for computer-aided pulmonary disease diagnosis by proposing a method using generative adversarial networks (GANs) for image translation, and it reported that the method is effective and outperforms state-of-the-art methods.
Lung image segmentation plays an important role in computer-aid pulmonary diseases diagnosis and treatment. This paper proposed a lung image segmentation method by generative adversarial networks. We employed a variety of generative adversarial networks and use its capability of image translation to perform image segmentation. The generative adversarial networks was employed to translate the original lung image to the segmented image. The generative adversarial networks based segmentation method was test on real lung image data set. Experimental results shows that the proposed method is effective and outperform state-of-the art method.