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Joint Registration And Segmentation Of Xray Images Using Generative Adversarial Networks

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 11046))

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Abstract

Medical image registration and segmentation are complementary functions and combining them can improve each other’s performance. Conventional deep learning (DL) based approaches tackle the two problems separately without leveraging their mutually beneficial information. We propose a DL based approach for joint registration and segmentation (JRS) of chest Xray images. Generative adversarial networks (GANs) are trained to register a floating image to a reference image by combining their segmentation map similarity with conventional feature maps. Intermediate segmentation maps from the GAN’s convolution layers are used in the training stage to generate the final segmentation mask at test time. Experiments on chest Xray images show that JRS gives better registration and segmentation performance than when solving them separately.

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Author information

Authors and Affiliations

  1. IBM Research Australia, Melbourne, VIC, Australia

    Dwarikanath Mahapatra, Suman Sedai & Rajib Chakravorty

  2. Monash University, Melbourne, VIC, Australia

    Zongyuan Ge

Authors
  1. Dwarikanath Mahapatra

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  2. Zongyuan Ge

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  3. Suman Sedai

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  4. Rajib Chakravorty

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Corresponding author

Correspondence toDwarikanath Mahapatra.

Editor information

Editors and Affiliations

  1. Nanjing University, Nanjing, China

    Yinghuan Shi

  2. 617A, Science Library, Korea University, Seoul, Korea (Republic of)

    Heung-Il Suk

  3. University of North Carolina at Chapel H, Chapel Hill, NC, USA

    Mingxia Liu

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Mahapatra, D., Ge, Z., Sedai, S., Chakravorty, R. (2018). Joint Registration And Segmentation Of Xray Images Using Generative Adversarial Networks. In: Shi, Y., Suk, HI., Liu, M. (eds) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science(), vol 11046. Springer, Cham. https://doi.org/10.1007/978-3-030-00919-9_9

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