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GANai: Standardizing CT Images using Generative Adversarial Network with Alternative Improvement

Gongbo Liang, Sajjad Fouladvand, Jie Zhang, Michael A. Brooks, Nathan Jacobs, Jin Chen
doi: https://doi.org/10.1101/460188
Gongbo Liang
1Institute for Biomedical Informatics, University of Kentucky, USA, Lexington, KY, USA
2Department of Computer Science, University of Kentucky, Lexington, KY, USA
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Sajjad Fouladvand
1Institute for Biomedical Informatics, University of Kentucky, USA, Lexington, KY, USA
2Department of Computer Science, University of Kentucky, Lexington, KY, USA
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Jie Zhang
3Department of Radiology, University of Kentucky, Lexington, KY, USA
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Michael A. Brooks
3Department of Radiology, University of Kentucky, Lexington, KY, USA
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Nathan Jacobs
2Department of Computer Science, University of Kentucky, Lexington, KY, USA
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Jin Chen
1Institute for Biomedical Informatics, University of Kentucky, USA, Lexington, KY, USA
2Department of Computer Science, University of Kentucky, Lexington, KY, USA
4Department of Internal Medicine, University of Kentucky, Lexington, KY, USA
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Abstract

Computed tomography (CT) is a widely-used diag-reproducibility regarding radiomic features, such as intensity, nostic image modality routinely used for assessing anatomical tissue characteristics. However, non-standardized imaging pro-tocols are commonplace, which poses a fundamental challenge in large-scale cross-center CT image analysis. One approach to address the problem is to standardize CT images using generative adversarial network models (GAN). GAN learns the data distribution of training images and generate synthesized images under the same distribution. However, existing GAN models are not directly applicable to this task mainly due to the lack of constraints on the mode of data to generate. Furthermore, they treat every image equally, but in real applications, some images are more difficult to standardize than the others. All these may lead to the lack-of-detail problem in CT image synthesis. We present a new GAN model called GANai to mitigate the differences in radiomic features across CT images captured using non-standard imaging protocols. Given source images, GANai composes new images by specifying a high-level goal that the image features of the synthesized images should be similar to those of the standard images. GANai introduces an alternative improvement training strategy to alternatively and steadily improve model performance. The new training strategy enables a series of technical improvements, including phase-specific loss functions, phase-specific training data, and the adoption of ensemble learning, leading to better model performance. The experimental results show that GANai is significantly better than the existing state-of-the-art image synthesis algorithms on CT image standardization. Also, it significantly improves the efficiency and stability of GAN model training.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 02, 2018.
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GANai: Standardizing CT Images using Generative Adversarial Network with Alternative Improvement
Gongbo Liang, Sajjad Fouladvand, Jie Zhang, Michael A. Brooks, Nathan Jacobs, Jin Chen
bioRxiv 460188; doi: https://doi.org/10.1101/460188
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GANai: Standardizing CT Images using Generative Adversarial Network with Alternative Improvement
Gongbo Liang, Sajjad Fouladvand, Jie Zhang, Michael A. Brooks, Nathan Jacobs, Jin Chen
bioRxiv 460188; doi: https://doi.org/10.1101/460188

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