Cascaded Feature Warping Network for Unsupervised Medical Image Registration

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 net...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-03
Main Authors: Zhang, Liutong, Zhou, Lei, Li, Ruiyang, Wang, Xianyu, Han, Boxuan, Liao, Hongen
Format: Article
Language:English
Subjects:
Coders
Formability
Image analysis
Image registration
Medical imaging
Modules
Pyramids
Registration
Warping
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422