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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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Authors and Affiliations
IBM Research Australia, Melbourne, VIC, Australia
Dwarikanath Mahapatra, Suman Sedai & Rajib Chakravorty
Monash University, Melbourne, VIC, Australia
Zongyuan Ge
- Dwarikanath Mahapatra
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- Zongyuan Ge
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- Suman Sedai
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- Rajib Chakravorty
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Correspondence toDwarikanath Mahapatra.
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Nanjing University, Nanjing, China
Yinghuan Shi
617A, Science Library, Korea University, Seoul, Korea (Republic of)
Heung-Il Suk
University of North Carolina at Chapel H, Chapel Hill, NC, USA
Mingxia Liu
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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: Shi, Y., Suk, HI., Liu, M. (eds) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science(), vol 11046. Springer, Cham. https://doi.org/10.1007/978-3-030-00919-9_9
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