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Joint Registration And Segmentation Of Xray Images Using Generative Adversarial Networks

  • Dwarikanath Mahapatra
  • Zongyuan Ge
  • Suman Sedai
  • Rajib Chakravorty
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)

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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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Dwarikanath Mahapatra
    • 1
  • Zongyuan Ge
    • 2
  • Suman Sedai
    • 1
  • Rajib Chakravorty
    • 1
  1. 1.IBM Research AustraliaMelbourneAustralia
  2. 2.Monash UniversityMelbourneAustralia

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