Lung image segmentation by generative adversarial networks

Jiaxin Cai; Hongfeng Zhu

doi:10.1117/12.2548153

27 November 2019 Lung image segmentation by generative adversarial networks

Jiaxin Cai, Hongfeng Zhu

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113210U (2019) https://doi.org/10.1117/12.2548153
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

Abstract

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.

Citation Download Citation

Jiaxin Cai and Hongfeng Zhu "Lung image segmentation by generative adversarial networks", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113210U (27 November 2019); https://doi.org/10.1117/12.2548153

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