Cascaded Feature Warping Network for Unsupervised Medical Image Registration

• URL: http://arxiv.org/abs/2103.08213v1
• Date: Mon, 15 Mar 2021 08:50:06 GMT
• Title: Cascaded Feature Warping Network for Unsupervised Medical Image Registration
• Authors: Liutong Zhang, Lei Zhou, Ruiyang Li, Xianyu Wang, Boxuan Han, Hongen Liao
• Abstract summary: We pre-sent a cascaded feature warping network to perform the coarse-to-fine registration. A shared-weights encoder network is adopted to generate the feature pyramids for the unaligned images. The results show that our method outperforms the state-of-the-art methods.
• Score: 11.052668687673998
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deformable image registration is widely utilized in medical image analysis, but most proposed methods fail in the situation of complex deformations. In this paper, we pre-sent a cascaded feature warping network to perform the coarse-to-fine registration. To achieve this, a shared-weights encoder network is adopted to generate the feature pyramids for the unaligned images. The feature warping registration module is then used to estimate the deformation field at each level. The coarse-to-fine manner is implemented by cascading the module from the bottom level to the top level. Furthermore, the multi-scale loss is also introduced to boost the registration performance. We employ two public benchmark datasets and conduct various experiments to evaluate our method. The results show that our method outperforms the state-of-the-art methods, which also demonstrates that the cascaded feature warping network can perform the coarse-to-fine registration effectively and efficiently.

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