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Lung image segmentation via generative adversarial networks
Jiaxin Cai1, Hongfeng Zhu1, Siyu Liu2
1School of Mathematics and Statistics, Xiamen University of Technology, Xiamen, China.
Introduction:
Lung image segmentation plays an important role in computer-aid pulmonary disease diagnosis and treatment.
Methods:
This paper explores the lung CT image segmentation method by generative adversarial networks. We employ a variety of generative adversarial networks and used their capability of image translation to perform image segmentation. The generative adversarial network is employed to translate the original lung image into the segmented image.
Results:
The generative adversarial networks-based segmentation method is tested on real lung image data set. Experimental results show that the proposed method outperforms the state-of-the-art method.
Discussion:
The generative adversarial networks-based method is effective for lung image segmentation.
