Spatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration

• URL: http://arxiv.org/abs/2303.06168v1
• Date: Fri, 10 Mar 2023 19:11:16 GMT
• Title: Spatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration
• Authors: Junyu Chen, Yihao Liu, Yufan He, Yong Du
• Abstract summary: We introduce an end-to-end framework that uses neural networks to learn a deformation regularizer directly from data. The proposed method is built upon a Transformer-based model, but it can be readily adapted to any network architecture.
• Score: 11.498623409184225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the past, optimization-based registration models have used spatially-varying regularization to account for deformation variations in different image regions. However, deep learning-based registration models have mostly relied on spatially-invariant regularization. Here, we introduce an end-to-end framework that uses neural networks to learn a spatially-varying deformation regularizer directly from data. The hyperparameter of the proposed regularizer is conditioned into the network, enabling easy tuning of the regularization strength. The proposed method is built upon a Transformer-based model, but it can be readily adapted to any network architecture. We thoroughly evaluated the proposed approach using publicly available datasets and observed a significant performance improvement while maintaining smooth deformation. The source code of this work will be made available after publication.

Related papers

• Space-Variant Total Variation boosted by learning techniques in few-view tomographic imaging [0.0]
  This paper focuses on the development of a space-variant regularization model for solving an under-determined linear inverse problem. The primary objective of the proposed model is to achieve a good balance between denoising and the preservation of fine details and edges. A convolutional neural network is designed, to approximate both the ground truth image and its gradient using an elastic loss function in its training.
  arXiv  Detail & Related papers  (2024-04-25T08:58:41Z)
• Equivariant Adaptation of Large Pretrained Models [20.687626756753563]
  We show that a canonicalization network can effectively be used to make a large pretrained network equivariant. Using dataset-dependent priors to inform the canonicalization function, we are able to make large pretrained models equivariant while maintaining their performance.
  arXiv  Detail & Related papers  (2023-10-02T21:21:28Z)
• Dynamic Kernel-Based Adaptive Spatial Aggregation for Learned Image Compression [63.56922682378755]
  We focus on extending spatial aggregation capability and propose a dynamic kernel-based transform coding. The proposed adaptive aggregation generates kernel offsets to capture valid information in the content-conditioned range to help transform. Experimental results demonstrate that our method achieves superior rate-distortion performance on three benchmarks compared to the state-of-the-art learning-based methods.
  arXiv  Detail & Related papers  (2023-08-17T01:34:51Z)
• Conditional Deformable Image Registration with Spatially-Variant and Adaptive Regularization [2.3419031955865517]
  We propose a learning-based registration approach based on a novel conditional spatially adaptive instance normalization (CSAIN) Experiments show that our proposed method outperforms the baseline approaches while achieving spatially-variant and adaptive regularization.
  arXiv  Detail & Related papers  (2023-03-19T16:12:06Z)
• Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching [26.77241999731105]
  We present a novel approach for computing correspondences between non-rigid objects by exploiting a reduced representation of deformation fields. By letting the network learn deformation parameters at a sparse set of positions in space (nodes), we reconstruct the continuous deformation field in a closed-form with guaranteed smoothness. Our model has high expressive power and is able to capture complex deformations.
  arXiv  Detail & Related papers  (2022-11-26T16:11:17Z)
• Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation [76.85274970052762]
  This paper is motivated by a proliferation of options of alignment regularizations. We evaluate the performances of several popular design choices along the dimensions of robustness and invariance. We also formally analyze the behavior of alignment regularization to complement our empirical study under assumptions we consider realistic.
  arXiv  Detail & Related papers  (2022-06-04T04:29:19Z)
• Frame Averaging for Equivariant Shape Space Learning [85.42901997467754]
  A natural way to incorporate symmetries in shape space learning is to ask that the mapping to the shape space (encoder) and mapping from the shape space (decoder) are equivariant to the relevant symmetries. We present a framework for incorporating equivariance in encoders and decoders by introducing two contributions.
  arXiv  Detail & Related papers  (2021-12-03T06:41:19Z)
• Self-supervised Augmentation Consistency for Adapting Semantic Segmentation [56.91850268635183]
  We propose an approach to domain adaptation for semantic segmentation that is both practical and highly accurate. We employ standard data augmentation techniques $-$ photometric noise, flipping and scaling $-$ and ensure consistency of the semantic predictions. We achieve significant improvements of the state-of-the-art segmentation accuracy after adaptation, consistent both across different choices of the backbone architecture and adaptation scenarios.
  arXiv  Detail & Related papers  (2021-04-30T21:32:40Z)
• Efficient Semantic Image Synthesis via Class-Adaptive Normalization [116.63715955932174]
  Class-adaptive normalization (CLADE) is a lightweight but equally-effective variant that is only adaptive to semantic class. We introduce intra-class positional map encoding calculated from semantic layouts to modulate the normalization parameters of CLADE. The proposed CLADE can be generalized to different SPADE-based methods while achieving comparable generation quality compared to SPADE.
  arXiv  Detail & Related papers  (2020-12-08T18:59:32Z)
• Deep Learning for Regularization Prediction in Diffeomorphic Image Registration [8.781861951759948]
  We introduce a novel framework that automatically determines the parameters controlling the smoothness of diffeomorphic transformations. We develop a predictive model based on deep convolutional neural networks (CNN) that learns the mapping between pairwise images and the regularization parameter of image registration. Experimental results show that our model not only predicts appropriate regularization parameters for image registration, but also improving the network training in terms of time and memory efficiency.
  arXiv  Detail & Related papers  (2020-11-28T22:56:44Z)
• Supervised Domain Adaptation using Graph Embedding [86.3361797111839]
  Domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. We propose a generic framework based on graph embedding. We show that the proposed approach leads to a powerful Domain Adaptation framework.
  arXiv  Detail & Related papers  (2020-03-09T12:25:13Z)

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.