CycleMorph: Cycle Consistent Unsupervised Deformable Image Registration
- URL: http://arxiv.org/abs/2008.05772v1
- Date: Thu, 13 Aug 2020 09:30:12 GMT
- Title: CycleMorph: Cycle Consistent Unsupervised Deformable Image Registration
- Authors: Boah Kim, Dong Hwan Kim, Seong Ho Park, Jieun Kim, June-Goo Lee, Jong
Chul Ye
- Abstract summary: We present a cycle-consistent deformable image registration. The cycle consistency enhances image registration performance by providing an implicit regularization to preserve topology during the deformation.
Experimental results on various datasets from medical and non-medical applications demonstrate that the proposed method provides effective and accurate registration on diverse image pairs within a few seconds.
- Score: 34.546992605648086
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Image registration is a fundamental task in medical image analysis. Recently,
deep learning based image registration methods have been extensively
investigated due to their excellent performance despite the ultra-fast
computational time. However, the existing deep learning methods still have
limitation in the preservation of original topology during the deformation with
registration vector fields. To address this issues, here we present a
cycle-consistent deformable image registration. The cycle consistency enhances
image registration performance by providing an implicit regularization to
preserve topology during the deformation. The proposed method is so flexible
that can be applied for both 2D and 3D registration problems for various
applications, and can be easily extended to multi-scale implementation to deal
with the memory issues in large volume registration. Experimental results on
various datasets from medical and non-medical applications demonstrate that the
proposed method provides effective and accurate registration on diverse image
pairs within a few seconds. Qualitative and quantitative evaluations on
deformation fields also verify the effectiveness of the cycle consistency of
the proposed method.
Related papers
- Breaking Modality Disparity: Harmonized Representation for Infrared and
Visible Image Registration [66.33746403815283]
We propose a scene-adaptive infrared and visible image registration.
We employ homography to simulate the deformation between different planes.
We propose the first ground truth available misaligned infrared and visible image dataset.
arXiv Detail & Related papers (2023-04-12T06:49:56Z) - Learning Homeomorphic Image Registration via Conformal-Invariant
Hyperelastic Regularisation [9.53064372566798]
We propose a novel framework for deformable image registration based on conformal-invariant properties.
Our regulariser enforces the deformation field yielding to be smooth, invertible and orientation-preserving.
We demonstrate, through numerical and visual experiments, that our framework is able to outperform current techniques for image registration.
arXiv Detail & Related papers (2023-03-14T17:47:18Z) - ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image
Registration [20.054872823030454]
Deformable image registration serves as an essential preprocessing step for neuroimaging data.
We propose a novel solution, called Anti-Blur Network (ABN), for multi-stage image registration.
arXiv Detail & Related papers (2022-12-06T19:21:43Z) - Joint segmentation and discontinuity-preserving deformable registration:
Application to cardiac cine-MR images [74.99415008543276]
Most deep learning-based registration methods assume that the deformation fields are smooth and continuous everywhere in the image domain.
We propose a novel discontinuity-preserving image registration method to tackle this challenge, which ensures globally discontinuous and locally smooth deformation fields.
A co-attention block is proposed in the segmentation component of the network to learn the structural correlations in the input images.
We evaluate our method on the task of intra-subject-temporal image registration using large-scale cinematic cardiac magnetic resonance image sequences.
arXiv Detail & Related papers (2022-11-24T23:45:01Z) - DiffuseMorph: Unsupervised Deformable Image Registration Along
Continuous Trajectory Using Diffusion Models [31.826844124173984]
We present a novel approach of diffusion model-based probabilistic image registration, called DiffuseMorph.
Our model learns the score function of the deformation between moving and fixed images.
Our method can provide flexible and accurate deformation with a capability of topology preservation.
arXiv Detail & Related papers (2021-12-09T08:41:23Z) - A Deep Discontinuity-Preserving Image Registration Network [73.03885837923599]
Most deep learning-based registration methods assume that the desired deformation fields are globally smooth and continuous.
We propose a weakly-supervised Deep Discontinuity-preserving Image Registration network (DDIR) to obtain better registration performance and realistic deformation fields.
We demonstrate that our method achieves significant improvements in registration accuracy and predicts more realistic deformations, in registration experiments on cardiac magnetic resonance (MR) images.
arXiv Detail & Related papers (2021-07-09T13:35:59Z) - Learning a Model-Driven Variational Network for Deformable Image
Registration [89.9830129923847]
VR-Net is a novel cascaded variational network for unsupervised deformable image registration.
It outperforms state-of-the-art deep learning methods on registration accuracy.
It maintains the fast inference speed of deep learning and the data-efficiency of variational model.
arXiv Detail & Related papers (2021-05-25T21:37:37Z) - Test-Time Training for Deformable Multi-Scale Image Registration [15.523457398508263]
Deep learning-based registration approaches such as VoxelMorph have been emerging and achieve competitive performance.
We construct a test-time training for deep deformable image registration to improve the generalization ability of conventional learning-based registration model.
arXiv Detail & Related papers (2021-03-25T03:22:59Z) - Deep Group-wise Variational Diffeomorphic Image Registration [3.0022455491411653]
We propose to extend current learning-based image registration to allow simultaneous registration of multiple images.
We present a general mathematical framework that enables both registration of multiple images to their viscous geodesic average and registration in which any of the available images can be used as a fixed image.
arXiv Detail & Related papers (2020-10-01T07:37:28Z) - An Auto-Context Deformable Registration Network for Infant Brain MRI [54.57017031561516]
We propose an infant-dedicated deep registration network that uses the auto-context strategy to gradually refine the deformation fields.
Our method estimates the deformation fields by invoking a single network multiple times for iterative deformation refinement.
Experimental results in comparison with state-of-the-art registration methods indicate that our method achieves higher accuracy while at the same time preserves the smoothness of the deformation fields.
arXiv Detail & Related papers (2020-05-19T06:00:13Z) - Learning Deformable Image Registration from Optimization: Perspective,
Modules, Bilevel Training and Beyond [62.730497582218284]
We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation.
We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
arXiv Detail & Related papers (2020-04-30T03:23:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.