Abstract
Deep learning has been extensively used in unsupervised deformable image registration. U-Net structures are often used to infer deformation fields from concatenated input images, and training is achieved by minimizing losses derived from image similarity and field regularization terms. However, the mechanism of multiresolution encoding and decoding with skip connections tends to mix up the spatial relationship between corresponding voxels or features. This paper proposes a multiresolution registration network (MRN) based on simple convolution layers at each resolution level and forms a framework mimicking the ideas of well-accepted traditional image registration algorithms, wherein deformations are solved at the lowest resolution and further refined level-by-level. Multiresolution image features can be directly fed into the network, and wavelet decomposition is employed to maintain rich features at low resolution. In addition, prior knowledge of deformations at the lowest resolution is modeled by kernel-PCA when the template image is fixed, and such a prior loss is employed for training at that level to better tolerate shape variability. The proposed algorithm can be directly used for group analysis or image labeling and potentially applied for registering any image pairs. We compared the performance of MRN with different settings, i.e., w/wo wavelet features, w/wo kernel-PCA losses, using brain magnetic resonance (MR) images, and the results showed better performance for the multiresolution representation and prior knowledge learning.
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Acknowledgement
This work was partially supported by the National Key Research and Development Program of China (2018YFC0116400) and the National Natural Science Foundation of China (62071176).
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Gu, D., Cao, X., Liu, G., Shen, D., Xue, Z. (2021). Multiresolution Registration Network (MRN) Hierarchy with Prior Knowledge Learning. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_7
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