Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation

• URL: http://arxiv.org/abs/2004.07624v1
• Date: Thu, 16 Apr 2020 12:24:27 GMT
• Title: Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation
• Authors: Yujia Zhou, Shumao Pang, Jun Cheng, Yuhang Sun, Yi Wu, Lei Zhao, Yaqin Liu, Zhentai Lu, Wei Yang, and Qianjin Feng
• Abstract summary: Deformation field estimation is an important and challenging issue in many medical image registration applications. In this study, we constructed pyramidal feature sets on moving and fixed images and used the warped moving and fixed features to estimate their "residual" deformation field at each scale. Our method improves the accuracy of the registration and the rationality of the deformation field.
• Score: 24.413236251959137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deformation field estimation is an important and challenging issue in many medical image registration applications. In recent years, deep learning technique has become a promising approach for simplifying registration problems, and has been gradually applied to medical image registration. However, most existing deep learning registrations do not consider the problem that when the receptive field cannot cover the corresponding features in the moving image and the fixed image, it cannot output accurate displacement values. In fact, due to the limitation of the receptive field, the 3 x 3 kernel has difficulty in covering the corresponding features at high/original resolution. Multi-resolution and multi-convolution techniques can improve but fail to avoid this problem. In this study, we constructed pyramidal feature sets on moving and fixed images and used the warped moving and fixed features to estimate their "residual" deformation field at each scale, called the Pyramidal Residual Deformation Field Estimation module (PRDFE-Module). The "total" deformation field at each scale was computed by upsampling and weighted summing all the "residual" deformation fields at all its previous scales, which can effectively and accurately transfer the deformation fields from low resolution to high resolution and is used for warping the moving features at each scale. Simulation and real brain data results show that our method improves the accuracy of the registration and the rationality of the deformation field.

Related papers

• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• 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)
• 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)
• Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration [33.72954116727303]
  We present a novel reinforcement learning-based framework that performs step-wise registration of medical images with large deformations. We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations.
  arXiv  Detail & Related papers  (2021-12-14T14:08:56Z)
• 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)
• Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks [11.4219428942199]
  We propose a deep Laplacian Pyramid Image Registration Network to solve the image registration optimization problem. Our method outperforms the existing methods by a significant margin while maintaining desirable diffeomorphic properties and promising registration speed.
  arXiv  Detail & Related papers  (2020-06-29T16:10:40Z)
• 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)
• NiLBS: Neural Inverse Linear Blend Skinning [59.22647012489496]
  We introduce a method to invert the deformations undergone via traditional skinning techniques via a neural network parameterized by pose. The ability to invert these deformations allows values (e.g., distance function, signed distance function, occupancy) to be pre-computed at rest pose, and then efficiently queried when the character is deformed.
  arXiv  Detail & Related papers  (2020-04-06T20:46:37Z)
• Learning Deformable Registration of Medical Images with Anatomical Constraints [4.397224870979238]
  Deformable image registration is a fundamental problem in the field of medical image analysis. We learn global non-linear representations of image anatomy using segmentation masks, and employ them to constraint the registration process. Our experiments show that the proposed anatomically constrained registration model produces more realistic and accurate results than state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-20T17:44:11Z)

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.