Networks for Joint Affine and Non-Parametric Image Registration

We introduce an end-to-end deep-learning framework for 3D medical image registration. In contrast to existing approaches, our framework combines two registration methods: an affine registration and a vector momentum-parameterized stationary velocity field (vSVF) model. Specifically, it consists of t...

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Vydáno v:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Ročník 2019; s. 4219 - 4228
Hlavní autoři: Shen, Zhengyang, Han, Xu, Xu, Zhenlin, Niethammer, Marc
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.06.2019
Témata:
Biological and Cell Microscopy
Biomedical imaging
Computational modeling
Deep Learning ; Optimization Methods
Image registration
Magnetic resonance imaging
Medical
Osteoarthritis
Pattern recognition
Smoothing methods
Three-dimensional displays
Transforms
Vectors
ISSN:1063-6919, 1063-6919
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Shrnutí:We introduce an end-to-end deep-learning framework for 3D medical image registration. In contrast to existing approaches, our framework combines two registration methods: an affine registration and a vector momentum-parameterized stationary velocity field (vSVF) model. Specifically, it consists of three stages. In the first stage, a multi-step affine network predicts affine transform parameters. In the second stage, we use a U-Net-like network to generate a momentum, from which a velocity field can be computed via smoothing. Finally, in the third stage, we employ a self-iterable map-based vSVF component to provide a non-parametric refinement based on the current estimate of the transformation map. Once the model is trained, a registration is completed in one forward pass. To evaluate the performance, we conducted longitudinal and cross-subject experiments on 3D magnetic resonance images (MRI) of the knee of the Osteoarthritis Initiative (OAI) dataset. Results show that our framework achieves comparable performance to state-of-the-art medical image registration approaches, but it is much faster, with a better control of transformation regularity including the ability to produce approximately symmetric transformations, and combining affine as well as non-parametric registration.
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ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2019.00435