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A Deep Learning Based Method for Low Dose Lung CT Noise Reduction

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Proceedings of 2019 Chinese Intelligent Systems Conference (CISC 2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 592))

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Abstract

A method based on convolutional neural network auto encoder-decoder for low dose lung CT image noise reduction is presented. This method integrated convolutional neural network, auto encoder-decoder, residual learning, parametric rectified linear unit (PReLU). Particularly, the term of structural similarity index (SSIM) added to the loss function. After training patch by patch, the model attains a promising performance compared to state of the art traditional and deep learning methods in visual effects and quantitative measurements.

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China (Grant 61401049).

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Authors and Affiliations

  1. College of Optoelectronic Engineering, Chongqing University, Chongqing, 400044, China

    Yinjin Ma, Peng Feng, Peng He, Zourong Long & Biao Wei

  2. School of Data Science, Tongren University, Guizhou, 554300, China

    Yinjin Ma

Authors
  1. Yinjin Ma
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  2. Peng Feng
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  3. Peng He
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  4. Zourong Long
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  5. Biao Wei
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Corresponding author

Correspondence to Biao Wei .

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Editors and Affiliations

  1. Beihang University, Beijing, China

    Yingmin Jia

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  3. University of Science and Technology Beijing, Beijing, China

    Weicun Zhang

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Ma, Y., Feng, P., He, P., Long, Z., Wei, B. (2020). A Deep Learning Based Method for Low Dose Lung CT Noise Reduction. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 592. Springer, Singapore. https://doi.org/10.1007/978-981-32-9682-4_68

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