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BIRNet: Brain image registration using dual-supervised fully convolutional networks.

08:00 EDT 22nd March 2019 | BioPortfolio

Summary of "BIRNet: Brain image registration using dual-supervised fully convolutional networks."

In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance. Since obtaining ground-truth deformation fields for training can be challenging, we design a fully convolutional network that is subject to dual-guidance: (1) Ground-truth guidance using deformation fields obtained by an existing registration method; and (2) Image dissimilarity guidance using the difference between the images after registration. The latter guidance helps avoid overly relying on the supervision from the training deformation fields, which could be inaccurate. For effective training, we further improve the deep convolutional network with gap filling, hierarchical loss, and multi-source strategies. Experiments on a variety of datasets show promising registration accuracy and efficiency compared with state-of-the-art methods.

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This article was published in the following journal.

Name: Medical image analysis
ISSN: 1361-8423
Pages: 193-206

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