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

19 Citations (Scopus)


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
Number of pages8
ISBN (Electronic)9783030009199
ISBN (Print)9783030009182
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

Publication series

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


ConferenceInternational Workshop on Machine Learning in Medical Imaging (MLMI) 2018
Abbreviated titleMLMI 2018
Internet address

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