End to End Unsupervised Rigid Medical Image Registration by Using Convolutional Neural Networks

End to End Unsupervised Rigid Medical Image Registration by Using Convolutional Neural Networks

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

10.1109/EMBC46164.2021.9630351

https://doi.org/10.1109/embc46164.2021.9630351

Huiying Liu, Yanling Chi, Jiawei Mao, Xiaoxiang Wu, Zhiqiang Liu, Yuyu Xu, Guibin Xu, Weimin Huang

medical image registration,

09 December 2021

H. Liu et al., "End to End Unsupervised Rigid Medical Image Registration by Using Convolutional Neural Networks," 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021, pp. 4064-4067, doi: 10.1109/EMBC46164.2021.9630351.

In this paper, we focus on the issue of rigid medical image registration using deep learning. Under ultrasound, the moving of some organs, e.g., liver and kidney, can be modeled as rigid motion. Therefore, when the ultrasound probe keeps stationary, the registration between frames can be modeled as rigid registration. We propose an unsupervised method with Convolutional Neural Networks. The network estimates from the input image pair the transform parameters first then the moving image is wrapped using the parameters. The loss is calculated between the registered image and the fixed image. Experiments on ultrasound data of kidney and liver verified that the method is capable of achieve higher accuracy compared with traditional methods and is much faster.

Publisher Copyright

This research / project is supported by the Agency for Science, Technology and Research - -
Grant Reference no. : ACCL/19-GAP035-R20H

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https://oar.a-star.edu.sg/communities-collections/articles/18090

2694-0604
2375-7477

978-1-7281-1179-7
978-1-7281-1178-0
978-1-7281-1180-3

Institute for Infocomm Research