Synthesis and texture manipulation of screening mammograms using conditional generative adversarial network

Dehan Kong; Yinhao Ren; Rui Hou; Lars J. Grimm; Jeffrey R. Marks; Joseph Y. Lo

doi:10.1117/12.2513125

13 March 2019 Synthesis and texture manipulation of screening mammograms using conditional generative adversarial network

Dehan Kong, Yinhao Ren, Rui Hou, Lars J. Grimm, Jeffrey R. Marks, Joseph Y. Lo

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109502M (2019) https://doi.org/10.1117/12.2513125
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Annotated data availability has always been a major limiting f actor for the development of algorithms in the field of computer aided diagnosis. The purpose of this study is to investigate the feasibility of using a conditional generative adversarial network (GAN) to synthesize high resolution mammography images with semantic control. We feed a binary mammographic texture map to the generator to synthesize a full-field digital-mammogram (FFDM). Our results show the generator quickly learned to grow anatomical details around the edges within the texture mask. However, we found the training unstable and the quality of generated images unsatisfactory due to the inherent limitation of latent space and sample space mapping by the pix2pix framework. In order to synthesize high resolution mammography images with semantic control, we identified the critical challenge is to build the efficient mappings of binary textures with a great variety of pattern realizations with the image domain.

Citation Download Citation

Dehan Kong, Yinhao Ren, Rui Hou, Lars J. Grimm, Jeffrey R. Marks, and Joseph Y. Lo "Synthesis and texture manipulation of screening mammograms using conditional generative adversarial network", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502M (13 March 2019); https://doi.org/10.1117/12.2513125

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