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

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

https://app.dimensions.ai/details/publication/pub.1107038102


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