Cascaded Feature Warping Network For Unsupervised Medical Image Registration

Zhang, Liutong; Zhou, Lei; Li, Ruiyang; Wang, Xianyu; Han, Boxuan; Liao, Hongen

doi:10.1109/isbi48211.2021.9433880

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…Zhao et al [1] proposed a recursive cascade network for deformable image registration. In a study by Zhang et al [2], a cascade feature deformation network was proposed as a method for performing registration from coarse to fine. Zhao et al [3] put forth an end-to-end cascade scheme for large displacement problems.…”

Section: Introductionmentioning

confidence: 99%

“…The following are the primary contributions: (1) We downsample the first deformation field in the cascaded network to obtain deformation fields of different scales, which provides the model with multi-scale information and helps to better obtain global and local features of the MRI image. (2) We introduce an attention mechanism that allows the model to selectively focus on specific regions, allowing for greater flexibility in adapting complex anatomical structures and registering complex small regions in MRI images. Experimental results have shown that medical image registration methods incorporating attention mechanisms exhibit higher registration accuracy on the 3D brain MRI datasets LPBA40 and HBN.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A deep learning model using localized attention mechanism for brain MRI registration

Zhu,

Zhang,

et al. 2024

Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024)

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Registration methods based on deep learning have been gotten popular for its performance and efficiency by automatically extracting deep features of images. Currently, the registration performance is further improved by using cascade networks. However, there is few studies pay attention to the registration of local complex areas of the brain. This paper combines the advantages of cascade networks and attention mechanism to solve the problem of registration in complex small areas of the brain by introducing a localized attention block. Specifically, we introduce an attention mechanism module (called Res-non-local-block), which consists of the attention mechanism (non-local-block), allowing the block to more flexibly adapt to complex anatomical structures and image changes. We also extract deformation fields of different scales by downsampling the predicted deformation fields to further improve the local registration performance. Experiments on the public datasets LPBA40 and HBN demonstrate that our proposed method exhibits higher registration accuracy on local areas of the brain than several popular deep learning based on registration models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deep learning model using localized attention mechanism for brain MRI registration

Zhu,

Zhang,

et al. 2024

Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024)

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“…After years of development, computer in-depth learning technology ( 28 ) and artificial intelligence technology ( 29 ) have made significant breakthroughs in theory and practice. They have made revolutionary progress in realizing the open sharing of medical information and using artificial intelligence to organize and analyze fragmented medical information.…”

Section: Introductionmentioning

confidence: 99%

Non-rigid Multi-Modal Medical Image Registration Based on Improved Maximum Mutual Information PV Image Interpolation Method

2022

Front. Public Health

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With the continuous improvement of medical imaging equipment, CT, MRI and PET images can obtain accurate anatomical information of the same patient site. However, due to the fuzziness of medical image physiological evaluation and the unhealthy understanding of objects, the registration effect of many methods is not ideal. Therefore, based on the medical image registration model of Partial Volume (PV) image interpolation method and rigid medical image registration method, this paper established the non-rigid registration model of maximum mutual information Novel Partial Volume (NPV) image interpolation method. The proposed NPV interpolation method uses the Davidon-Fletcher-Powell algorithm (DFP) algorithm optimization method to solve the transformation parameter matrix and realize the accurate transformation of the floating image. In addition, the cubic B-spline is used as the kernel function to improve the image interpolation, which effectively improves the accuracy of the registration image. Finally, the proposed NPV method is compared with the PV interpolation method through the human brain CT-MRI-PET image to obtain a clear CT-MRI-PET image. The results show that the proposed NPV method has higher accuracy, better robustness, and easier realization. The model should also have guiding significance in face recognition and fingerprint recognition.

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“…Furthermore, the multi-stage cascaded approach also tends to cause error accumulation. In addition, existing progressive registration methods (Cheng et al, 2019 ; Zhou et al, 2020 ; Kim et al, 2021 ; Zhang et al, 2021 ) integrate the displacement vector field by direct addition. However, the same position on the two adjacent displacement vector fields may not be the displacement of the same corresponding point, so it is unreasonable to obtain the total deformation field by directly summing the multiple deformation fields.…”

Section: Introductionmentioning

confidence: 99%

Progressive 3D biomedical image registration network based on deep self-calibration

Sun

Miao

et al. 2022

Front. Neuroinform.

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Three dimensional deformable image registration (DIR) is a key enabling technique in building digital neuronal atlases of the brain, which can model the local non-linear deformation between a pair of biomedical images and align the anatomical structures of different samples into one spatial coordinate system. And thus, the DIR is always conducted following a preprocessing of global linear registration to remove the large global deformations. However, imperfect preprocessing may leave some large non-linear deformations that cannot be handled well by existing DIR methods. The recently proposed cascaded registration network gives a primary solution to deal with such large non-linear deformations, but still suffers from loss of image details caused by continuous interpolation (information loss problem). In this article, a progressive image registration strategy based on deep self-calibration is proposed to deal with the large non-linear deformations without causing information loss and introducing additional parameters. More importantly, we also propose a novel hierarchical registration strategy to quickly achieve accurate multi-scale progressive registration. In addition, our method can implicitly and reasonably implement dynamic dataset augmentation. We have evaluated the proposed method on both optical and MRI image datasets with obtaining promising results, which demonstrate the superior performance of the proposed method over several other state-of-the-art approaches for deformable image registration.

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Cascaded Feature Warping Network For Unsupervised Medical Image Registration

Cited by 10 publications

References 13 publications

A deep learning model using localized attention mechanism for brain MRI registration

A deep learning model using localized attention mechanism for brain MRI registration

Non-rigid Multi-Modal Medical Image Registration Based on Improved Maximum Mutual Information PV Image Interpolation Method

Progressive 3D biomedical image registration network based on deep self-calibration

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