A Learning Framework for Diffeomorphic Image Registration based on Quasi-conformal Geometry

• URL: http://arxiv.org/abs/2110.10580v1
• Date: Wed, 20 Oct 2021 14:23:24 GMT
• Title: A Learning Framework for Diffeomorphic Image Registration based on Quasi-conformal Geometry
• Authors: Qiguang Chen, Zhiwen Li, Lok Ming Lui
• Abstract summary: We propose the quasi-conformal registration network (QCRegNet), an unsupervised learning framework, to obtain diffeomorphic 2D image registrations. QCRegNet consists of the estimator network and the Beltrami solver network (BSNet) Results show that the registration accuracy is comparable to state-of-the-art methods and diffeomorphism is to a great extent guaranteed.
• Score: 1.2891210250935146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image registration, the process of defining meaningful correspondences between images, is essential for various image analysis tasks, especially medical imaging. Numerous learning-based methods, notably convolutional neural networks (CNNs), for deformable image registration proposed in recent years have demonstrated the feasibility and superiority of deep learning techniques for registration problems. Besides, compared to traditional algorithms' optimization scheme of the objective function for each image pair, learning-based algorithms are several orders of magnitude faster. However, these data-driven methods without proper constraint on the deformation field will easily lead to topological foldings. To tackle this problem, We propose the quasi-conformal registration network (QCRegNet), an unsupervised learning framework, to obtain diffeomorphic 2D image registrations with large deformations based on quasi-conformal (QC) map, an orientation-preserving homeomorphism between two manifolds. The basic idea is to design a CNN mapping image pairs to deformation fields. QCRegNet consists of the estimator network and the Beltrami solver network (BSNet). The estimator network takes image pair as input and outputs the Beltrami coefficient (BC). The BC, which captures conformal distortion of a QC map and guarantees the bijectivity, will then be input to the BSNet, a task-independent network which reconstructs the desired QC map. Furthermore, we reduce the number of network parameters and computational complexity by utilizing Fourier approximation to compress BC. Experiments have been carried out on different data such as underwater and medical images. Registration results show that the registration accuracy is comparable to state-of-the-art methods and diffeomorphism is to a great extent guaranteed compared to other diffeomorphic registration algorithms.

Related papers

• MsMorph: An Unsupervised pyramid learning network for brain image registration [4.000367245594772]
  MsMorph is an image registration framework aimed at mimicking the manual process of registering image pairs. It decodes semantic information at different scales and continuously compen-sates for the predicted deformation field. The proposed method simulates the manual approach to registration, focusing on different regions of the image pairs and their neighborhoods.
  arXiv  Detail & Related papers  (2024-10-23T19:20:57Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts [75.51482952586773]
  deep neural networks generally require plenty of labeled training data and are vulnerable to domain shifts between training and test data. We present a novel approach to geometric domain adaptation for image registration, adapting a model from a labeled source to an unlabeled target domain. Our method consistently improves on the baseline model by 50%/47% while even matching the accuracy of models trained on target data.
  arXiv  Detail & Related papers  (2022-07-01T12:16:42Z)
• Non-iterative Coarse-to-fine Registration based on Single-pass Deep Cumulative Learning [11.795108660250843]
  We propose a Non-Iterative Coarse-to-finE registration network (NICE-Net) for deformable image registration. NICE-Net can outperform state-of-the-art iterative deep registration methods while only requiring similar runtime to non-iterative methods.
  arXiv  Detail & Related papers  (2022-06-25T08:34:59Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• A training-free recursive multiresolution framework for diffeomorphic deformable image registration [6.929709872589039]
  We propose a novel diffeomorphic training-free approach for deformable image registration. The proposed architecture is simple in design. The moving image is warped successively at each resolution and finally aligned to the fixed image. The entire system is end-to-end and optimized for each pair of images from scratch.
  arXiv  Detail & Related papers  (2022-02-01T15:17:17Z)
• Deformable Image Registration with Deep Network Priors: a Study on Longitudinal PET Images [0.5949967357689445]
  Inspired by Deep Image Prior, this paper introduces a different use of deep architectures as regularizers to tackle the image registration question. We propose a subject-specific deformable registration method called MIRRBA, relying on a deep pyramidal architecture to be the prior model constraining the deformation field. We demonstrate the regularizing power of deep architectures and present new elements to understand the role of the architecture in deep learning methods for registration.
  arXiv  Detail & Related papers  (2021-11-22T10:58:14Z)
• Learning to Learn Parameterized Classification Networks for Scalable Input Images [76.44375136492827]
  Convolutional Neural Networks (CNNs) do not have a predictable recognition behavior with respect to the input resolution change. We employ meta learners to generate convolutional weights of main networks for various input scales. We further utilize knowledge distillation on the fly over model predictions based on different input resolutions.
  arXiv  Detail & Related papers  (2020-07-13T04:27:25Z)
• CoMIR: Contrastive Multimodal Image Representation for Registration [4.543268895439618]
  We propose contrastive coding to learn shared, dense image representations, referred to as CoMIRs (Contrastive Multimodal Image Representations) CoMIRs enable the registration of multimodal images where existing registration methods often fail due to a lack of sufficiently similar image structures.
  arXiv  Detail & Related papers  (2020-06-11T10:51:33Z)
• 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.