Abstract
Affine registration has recently been formulated using deep learning frameworks to establish spatial correspondences between different images. In this work, we propose a new unsupervised model that investigates two new strategies to tackle fundamental problems related to affine registration. More specifically, the new model 1) has the advantage to explicitly learn specific geometric transformation parameters (e.g. translations, rotation, scaling and shearing); and 2) can effectively understand the context between the images via cross-stitch units allowing feature exchange. The proposed model is evaluated on two two-dimensional X-ray datasets and a three-dimensional CT dataset. Our experimental results show that our model not only outperforms state-of-art approaches and also can predict specific transformation parameters. Our core source code is made available online\(^{1}\)(\(^{1}\)https://github.com/xuuuuuuchen/PASTA).
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Notes
- 1.
\(\mathbf{A} _{2D} = \begin{bmatrix}a_{1} &{} a_{2} &{} a_{3}\\ a_{4} &{} a_{5} &{} a_{6}\\ 0 &{} 0 &{} 1\end{bmatrix}\) and \(\mathbf{A} _{3D} = \begin{bmatrix} a_{1} &{} a_{2} &{} a_{3} &{} a_{4}\\ a_{5} &{} a_{6} &{} a_{7} &{} a_{8}\\ a_{9} &{} a_{10} &{} a_{11} &{} a_{12}\\ 0 &{} 0 &{} 0 &{} 1 \end{bmatrix}\).
- 2.
\(\mathbf{A} \) is subject to the composition order. In this work, we use the order shown in Eq. (1).
- 3.
- 4.
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Acknowledgments
Xu Chen is funded by a studentship jointly funded by the Vascular Surgery Research Fund in Liverpool and Institute of Life Course and Medical Sciences, University of Liverpool, and partially funded by The Great Britain-China Educational Trust (no.269944) administered by the Great Britain-China Centre.
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Chen, X., Meng, Y., Zhao, Y., Williams, R., Vallabhaneni, S.R., Zheng, Y. (2021). Learning Unsupervised Parameter-Specific Affine Transformation for Medical Images Registration. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12904. Springer, Cham. https://doi.org/10.1007/978-3-030-87202-1_3
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