Joint Registration And Segmentation Of Xray Images Using Generative Adversarial Networks View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018

AUTHORS

Dwarikanath Mahapatra , Zongyuan Ge , Suman Sedai , Rajib Chakravorty

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. More... »

PAGES

73-80

References to SciGraph publications

  • 2010. Joint Registration and Segmentation of Dynamic Cardiac Perfusion Images Using MRFs in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2010
  • 2017. SVF-Net: Learning Deformable Image Registration Using Shape Matching in MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION − MICCAI 2017
  • 2017. Progressive and Multi-path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION − MICCAI 2017
  • 2017. Boundary-Aware Fully Convolutional Network for Brain Tumor Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION − MICCAI 2017
  • 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2015
  • Book

    TITLE

    Machine Learning in Medical Imaging

    ISBN

    978-3-030-00918-2
    978-3-030-00919-9

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-030-00919-9_9

    DOI

    http://dx.doi.org/10.1007/978-3-030-00919-9_9

    DIMENSIONS

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