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Proceedings Paper

Lung image segmentation by generative adversarial networks
Author(s): Jiaxin Cai; Hongfeng Zhu
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Paper 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.

Paper Details

Date Published: 27 November 2019
PDF: 6 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113210U (27 November 2019); doi: 10.1117/12.2548153
Show Author Affiliations
Jiaxin Cai, Xiamen Univ. of Technology (China)
Hongfeng Zhu, Xiamen Univ. of Technology (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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