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Using a Conditional Generative Adversarial Network (cGAN) for Prostate Segmentation

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Medical Image Understanding and Analysis (MIUA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1065))

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Abstract

Prostate cancer is the second most commonly diagnosed cancer among men and currently multi-parametric MRI is a promising imaging technique used for clinical workup of prostate cancer. Accurate detection and localisation of the prostate tissue boundary on various MRI scans can be helpful for obtaining a region of interest for Computer Aided Diagnosis systems. In this paper, we present a fully automated detection and segmentation pipeline using a conditional Generative Adversarial Network (cGAN). We investigated the robustness of the cGAN model against adding Gaussian noise or removing noise from the training data. Based on the detection and segmentation metrics, de-noising did not show a significant improvement. However, by including noisy images in the training data, the detection and segmentation performance was improved in each 3D modality, which resulted in comparable to state-of-the-art results.

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Notes

  1. 1.

    https://phillipi.github.io/pix2pix.

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Acknowledgments

The authors would like to acknowledge Dr. Alun Jones and Sandy Spence for their support and maintenance of the GPU and the computer systems used for this research.

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Authors and Affiliations

  1. Department of Computer Science, Aberystwyth University, Aberystwyth, UK

    Amelie Grall, Azam Hamidinekoo & Reyer Zwiggelaar

  2. Department of Radiology, Norwich & Norfolk University Hospital, Norwich, UK

    Paul Malcolm

Authors
  1. Amelie Grall
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  2. Azam Hamidinekoo
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  3. Paul Malcolm
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  4. Reyer Zwiggelaar
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Corresponding author

Correspondence to Azam Hamidinekoo .

Editor information

Editors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Yalin Zheng

  2. University of Liverpool, Liverpool, UK

    Bryan M. Williams

  3. University of Liverpool, Liverpool, UK

    Ke Chen

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Grall, A., Hamidinekoo, A., Malcolm, P., Zwiggelaar, R. (2020). Using a Conditional Generative Adversarial Network (cGAN) for Prostate Segmentation. In: Zheng, Y., Williams, B., Chen, K. (eds) Medical Image Understanding and Analysis. MIUA 2019. Communications in Computer and Information Science, vol 1065. Springer, Cham. https://doi.org/10.1007/978-3-030-39343-4_2

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  • DOI: https://doi.org/10.1007/978-3-030-39343-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39342-7

  • Online ISBN: 978-3-030-39343-4

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