Generating anthropomorphic phantoms using fully unsupervised deformable image registration with convolutional neural networks
Published in Medical Physics, 2020
We treat CNN as an optimization tool that iteratively minimizes the loss function via reparametrization in each iteration. This means that the algorithm is fully unsupervised and thus no prior training is required. We generate phantom variations by warpping an XCAT phantom to capture the anatomical variations within the real human CT images.
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Citation:
APA:
'Chen, J., Li, Y., Du, Y. and Frey, E.C. (2020), Generating anthropomorphic phantoms using
fully unsupervised deformable image registration with convolutional neural networks. <i>Medical Physics</i>. https://doi.org/10.1002/mp.14545'
BibTex:
@article{chen2020phantoms,
author = {Chen, Junyu and Li, Ye and Du, Yong and Frey, Eric C.},
title = {Generating Anthropomorphic Phantoms Using Fully Unsupervised Deformable Image Registration with Convolutional Neural Networks},
journal = {Medical Physics},
volume = {n/a},
number = {n/a},
pages = {},
doi = {10.1002/mp.14545},
url = {https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.14545},
eprint = {https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14545},
}