Joint registration and segmentation of xray images using generative adversarial networks

Dwarikanath Mahapatra, Zongyuan Ge, Suman Sedai, Rajib Chakravorty

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging
Subtitle of host publication9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings
EditorsYinghuan Shi, Heung-Il Suk, Mingxia Liu
Place of PublicationCham Switzerland
PublisherSpringer
Pages73-80
Number of pages8
Volume11046
ISBN (Electronic)9783030009199
ISBN (Print)9783030009182
DOIs
Publication statusPublished - 1 Jan 2018
EventInternational Workshop on Machine Learning in Medical Imaging (MLMI) 2018 - Granada Conference Centre, Granada, Spain
Duration: 16 Sep 201816 Sep 2018
Conference number: 9th
http://mlmi2018.web.unc.edu/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11046
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Workshop on Machine Learning in Medical Imaging (MLMI) 2018
Abbreviated titleMLMI 2018
CountrySpain
CityGranada
Period16/09/1816/09/18
Internet address

Cite this

Mahapatra, D., Ge, Z., Sedai, S., & Chakravorty, R. (2018). Joint registration and segmentation of xray images using generative adversarial networks. In Y. Shi, H-I. Suk, & M. Liu (Eds.), Machine Learning in Medical Imaging: 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings (Vol. 11046, pp. 73-80). (Lecture Notes in Computer Science ; Vol. 11046). Cham Switzerland: Springer. https://doi.org/10.1007/978-3-030-00919-9_9
Mahapatra, Dwarikanath ; Ge, Zongyuan ; Sedai, Suman ; Chakravorty, Rajib. / Joint registration and segmentation of xray images using generative adversarial networks. Machine Learning in Medical Imaging: 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings. editor / Yinghuan Shi ; Heung-Il Suk ; Mingxia Liu. Vol. 11046 Cham Switzerland : Springer, 2018. pp. 73-80 (Lecture Notes in Computer Science ).
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title = "Joint registration and segmentation of xray images using generative adversarial networks",
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.",
author = "Dwarikanath Mahapatra and Zongyuan Ge and Suman Sedai and Rajib Chakravorty",
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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 Y Shi, H-I Suk & M Liu (eds), Machine Learning in Medical Imaging: 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings. vol. 11046, Lecture Notes in Computer Science , vol. 11046, Springer, Cham Switzerland, pp. 73-80, International Workshop on Machine Learning in Medical Imaging (MLMI) 2018, Granada, Spain, 16/09/18. https://doi.org/10.1007/978-3-030-00919-9_9

Joint registration and segmentation of xray images using generative adversarial networks. / Mahapatra, Dwarikanath; Ge, Zongyuan; Sedai, Suman; Chakravorty, Rajib.

Machine Learning in Medical Imaging: 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings. ed. / Yinghuan Shi; Heung-Il Suk; Mingxia Liu. Vol. 11046 Cham Switzerland : Springer, 2018. p. 73-80 (Lecture Notes in Computer Science ; Vol. 11046).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AB - 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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Mahapatra D, Ge Z, Sedai S, Chakravorty R. Joint registration and segmentation of xray images using generative adversarial networks. In Shi Y, Suk H-I, Liu M, editors, Machine Learning in Medical Imaging: 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings. Vol. 11046. Cham Switzerland: Springer. 2018. p. 73-80. (Lecture Notes in Computer Science ). https://doi.org/10.1007/978-3-030-00919-9_9