computer-vision-10

Unsupervised Image Registration

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation

M. Niethammer, Zhenlin Xu•Tue Apr 16 2019

A deep learning framework that jointly learns networks for image registration and image segmentation, which achieves large simultaneous improvements of segmentation and registration accuracy (over independently trained networks) and allows training high-quality models with very limited training data.

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DRMIME: Differentiable Mutual Information and Matrix Exponential for Multi-Resolution Image Registration

Abhishek Nan, Matthew Tennant, Uriel Rubin, Nilanjan Ray•Fri Jan 24 2020

The novelty here is that rather than using traditional ways of approximating MI, this work uses a neural estimator called MINE and supplement it with matrix exponential for transformation matrix computation, which leads to improved results as compared to the standard algorithms available out-of-the-box in state-of the-art image registration toolboxes.

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FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow

Sergiu Mocanu, A. Moody, A. Khademi•Sat Jan 23 2021

Experimental results show that FlowReg is able to obtain high intensity and spatial similarity between the moving and the fixed volumes while maintaining the shape and structure of anatomy and pathology.

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Leveraging unsupervised image registration for discovery of landmark shape descriptor

Shireen Elhabian, Riddhish Bhalodia, L. Kavan, R. Whitaker•Thu Jul 08 2021

This paper proposes a self-supervised deep learning approach for discovering landmarks from images that can directly be used as a shape descriptor for subsequent analysis, and proposes a regularization term that allows for robust optimization of the neural network and ensures that the landmarks uniformly span the image domain.

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RFR-WWANet: Weighted Window Attention-Based Recovery Feature Resolution Network for Unsupervised Image Registration

Mingrui Ma, Lei Song, Gui-Xian Liu, Tao Wang, Weijie Wang•Sat May 06 2023

The proposed unsupervised deformable image registration model, named RFR-WWANet, detects the long-range correlations, and facilitates meaningful semantic relevance of anatomical structures, and achieves significant improvements over the current state-of-the-art methods.

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Symmetric Transformer-based Network for Unsupervised Image Registration

Mingrui Ma, Lei Song, Yuanbo Xu, Gui-Xian Liu•Wed Apr 27 2022

A convolution-based efficient multi-head self-attention (CEMSA) block is proposed, which reduces the parameters of the traditional Transformer and captures local spatial context information for reducing semantic ambiguity in the attention mechanism.

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Fourier-Net: Fast Image Registration with Band-limited Deformation

Jinming Duan, Wenqi Lu, Xiaogang Jia, Joseph Bartlett, Wei Chen, Siyang Song, T. Zhang, Xinxing Cheng, Zhaowen Qiu•Mon Nov 28 2022

The Fourier-Net is proposed, replacing the expansive path in a U-Net style network with a parameter-free model-driven decoder, allowing it to contain fewer parameters and computational operations, resulting in faster inference speeds.

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Fourier-Net+: Leveraging Band-Limited Representation for Efficient 3D Medical Image Registration

Jinming Duan, Xinlei Jia, Alexander J H Thorley, A. Gómez, Wenqi Lu, D. Kotecha•Wed Jul 05 2023

Fourier-Net+ enables the efficient training of large-scale 3D registration on low-VRAM GPUs and achieves comparable results with current state-of-the art methods, while exhibiting faster inference speeds, lower memory footprint, and fewer multiply-add operations.

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Residual Aligner-based Network (RAN): Motion-separable structure for coarse-to-fine discontinuous deformable registration

Baoru Huang, Jian-Qing Zheng, Ziyang Wang, Ngee Han Lim, B. W. Papież•Sun Mar 06 2022

A new Motion Separable backbone is exploited to capture the separate motion, with a theoretical analysis of the upper bound of the motions' discontinuity provided, and a novel Residual Aligner module was used to disentangle and refine the predicted motions across the multiple neighboring objects/organs.

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