Dual-stream pyramid registration network

•We propose a Dual-stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D medical image registration.•We design a two-stream 3D encoder-decoder network that computes two convolutional feature pyramids separately from two input volumes.•We propose sequential pyramid registra...

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Veröffentlicht in:	Medical image analysis Jg. 78; S. 102379
Hauptverfasser:	Kang, Miao, Hu, Xiaojun, Huang, Weilin, Scott, Matthew R., Reyes, Mauricio
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.05.2022 Elsevier BV
Schlagworte:	3D segmentation Annotations Benchmarks Brain Brain MRI Coders Decoding Deformable registration Encoder-decoder network Encoders-Decoders Humans Image Processing, Computer-Assisted Image registration Image segmentation Imaging, Three-Dimensional Magnetic Resonance Imaging Medical image registration Modules Neural Networks, Computer Neuroimaging Pyramids Registration Tomography, X-Ray Computed Warping 3D segmentation Brain MRI Encoder-decoder network Deformable registration Medical image registration
ISSN:	1361-8415, 1361-8423, 1361-8423
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Abstract	•We propose a Dual-stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D medical image registration.•We design a two-stream 3D encoder-decoder network that computes two convolutional feature pyramids separately from two input volumes.•We propose sequential pyramid registration where a sequence of pyramid registration (RP) modules is designed to predict multi-level registration fields directly from the decoding feature pyramids.•The PR modules can be further enhanced by computing local 3D correlations between the feature pyramids, resulting in the improved Dual-PRNet++.•Our Dual-PRNet++ can be integrated into a 3D segmentation framework for joint registration and segmentation, by precisely warping voxel-level annotations. [Display omitted] We propose a Dual-stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D brain image registration. Unlike recent CNN-based registration approaches, such as VoxelMorph, which computes a registration field from a pair of 3D volumes using a single-stream network, we design a two-stream architecture able to estimate multi-level registration fields sequentially from a pair of feature pyramids. Our main contributions are: (i) we design a two-stream 3D encoder-decoder network that computes two convolutional feature pyramids separately from two input volumes; (ii) we propose sequential pyramid registration where a sequence of pyramid registration (PR) modules is designed to predict multi-level registration fields directly from the decoding feature pyramids. The registration fields are refined gradually in a coarse-to-fine manner via sequential warping, which equips the model with a strong capability for handling large deformations; (iii) the PR modules can be further enhanced by computing local 3D correlations between the feature pyramids, resulting in the improved Dual-PRNet++ able to aggregate rich detailed anatomical structure of the brain; (iv) our Dual-PRNet++ can be integrated into a 3D segmentation framework for joint registration and segmentation, by precisely warping voxel-level annotations. Our methods are evaluated on two standard benchmarks for brain MRI registration, where Dual-PRNet++ outperforms the state-of-the-art approaches by a large margin, i.e., improving recent VoxelMorph from 0.511 to 0.748 (Dice score) on the Mindboggle101 dataset. In addition, we further demonstrate that our methods can greatly facilitate the segmentation task in a joint learning framework, by leveraging limited annotations.
AbstractList	•We propose a Dual-stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D medical image registration.•We design a two-stream 3D encoder-decoder network that computes two convolutional feature pyramids separately from two input volumes.•We propose sequential pyramid registration where a sequence of pyramid registration (RP) modules is designed to predict multi-level registration fields directly from the decoding feature pyramids.•The PR modules can be further enhanced by computing local 3D correlations between the feature pyramids, resulting in the improved Dual-PRNet++.•Our Dual-PRNet++ can be integrated into a 3D segmentation framework for joint registration and segmentation, by precisely warping voxel-level annotations. [Display omitted] We propose a Dual-stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D brain image registration. Unlike recent CNN-based registration approaches, such as VoxelMorph, which computes a registration field from a pair of 3D volumes using a single-stream network, we design a two-stream architecture able to estimate multi-level registration fields sequentially from a pair of feature pyramids. Our main contributions are: (i) we design a two-stream 3D encoder-decoder network that computes two convolutional feature pyramids separately from two input volumes; (ii) we propose sequential pyramid registration where a sequence of pyramid registration (PR) modules is designed to predict multi-level registration fields directly from the decoding feature pyramids. The registration fields are refined gradually in a coarse-to-fine manner via sequential warping, which equips the model with a strong capability for handling large deformations; (iii) the PR modules can be further enhanced by computing local 3D correlations between the feature pyramids, resulting in the improved Dual-PRNet++ able to aggregate rich detailed anatomical structure of the brain; (iv) our Dual-PRNet++ can be integrated into a 3D segmentation framework for joint registration and segmentation, by precisely warping voxel-level annotations. Our methods are evaluated on two standard benchmarks for brain MRI registration, where Dual-PRNet++ outperforms the state-of-the-art approaches by a large margin, i.e., improving recent VoxelMorph from 0.511 to 0.748 (Dice score) on the Mindboggle101 dataset. In addition, we further demonstrate that our methods can greatly facilitate the segmentation task in a joint learning framework, by leveraging limited annotations. We propose a Dual-stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D brain image registration. Unlike recent CNN-based registration approaches, such as VoxelMorph, which computes a registration field from a pair of 3D volumes using a single-stream network, we design a two-stream architecture able to estimate multi-level registration fields sequentially from a pair of feature pyramids. Our main contributions are: (i) we design a two-stream 3D encoder-decoder network that computes two convolutional feature pyramids separately from two input volumes; (ii) we propose sequential pyramid registration where a sequence of pyramid registration (PR) modules is designed to predict multi-level registration fields directly from the decoding feature pyramids. The registration fields are refined gradually in a coarse-to-fine manner via sequential warping, which equips the model with a strong capability for handling large deformations; (iii) the PR modules can be further enhanced by computing local 3D correlations between the feature pyramids, resulting in the improved Dual-PRNet++ able to aggregate rich detailed anatomical structure of the brain; (iv) our Dual-PRNet ++ can be integrated into a 3D segmentation framework for joint registration and segmentation, by precisely warping voxel-level annotations. Our methods are evaluated on two standard benchmarks for brain MRI registration, where Dual-PRNet ++ outperforms the state-of-the-art approaches by a large margin, i.e., improving recent VoxelMorph from 0.511 to 0.748 (Dice score) on the Mindboggle101 dataset. In addition, we further demonstrate that our methods can greatly facilitate the segmentation task in a joint learning framework, by leveraging limited annotations. We propose a Dual-stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D brain image registration. Unlike recent CNN-based registration approaches, such as VoxelMorph, which computes a registration field from a pair of 3D volumes using a single-stream network, we design a two-stream architecture able to estimate multi-level registration fields sequentially from a pair of feature pyramids. Our main contributions are: (i) we design a two-stream 3D encoder-decoder network that computes two convolutional feature pyramids separately from two input volumes; (ii) we propose sequential pyramid registration where a sequence of pyramid registration (PR) modules is designed to predict multi-level registration fields directly from the decoding feature pyramids. The registration fields are refined gradually in a coarse-to-fine manner via sequential warping, which equips the model with a strong capability for handling large deformations; (iii) the PR modules can be further enhanced by computing local 3D correlations between the feature pyramids, resulting in the improved Dual-PRNet++ able to aggregate rich detailed anatomical structure of the brain; (iv) our Dual-PRNet++ can be integrated into a 3D segmentation framework for joint registration and segmentation, by precisely warping voxel-level annotations. Our methods are evaluated on two standard benchmarks for brain MRI registration, where Dual-PRNet++ outperforms the state-of-the-art approaches by a large margin, i.e., improving recent VoxelMorph from 0.511 to 0.748 (Dice score) on the Mindboggle101 dataset. In addition, we further demonstrate that our methods can greatly facilitate the segmentation task in a joint learning framework, by leveraging limited annotations.We propose a Dual-stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D brain image registration. Unlike recent CNN-based registration approaches, such as VoxelMorph, which computes a registration field from a pair of 3D volumes using a single-stream network, we design a two-stream architecture able to estimate multi-level registration fields sequentially from a pair of feature pyramids. Our main contributions are: (i) we design a two-stream 3D encoder-decoder network that computes two convolutional feature pyramids separately from two input volumes; (ii) we propose sequential pyramid registration where a sequence of pyramid registration (PR) modules is designed to predict multi-level registration fields directly from the decoding feature pyramids. The registration fields are refined gradually in a coarse-to-fine manner via sequential warping, which equips the model with a strong capability for handling large deformations; (iii) the PR modules can be further enhanced by computing local 3D correlations between the feature pyramids, resulting in the improved Dual-PRNet++ able to aggregate rich detailed anatomical structure of the brain; (iv) our Dual-PRNet++ can be integrated into a 3D segmentation framework for joint registration and segmentation, by precisely warping voxel-level annotations. Our methods are evaluated on two standard benchmarks for brain MRI registration, where Dual-PRNet++ outperforms the state-of-the-art approaches by a large margin, i.e., improving recent VoxelMorph from 0.511 to 0.748 (Dice score) on the Mindboggle101 dataset. In addition, we further demonstrate that our methods can greatly facilitate the segmentation task in a joint learning framework, by leveraging limited annotations. We propose a Dual-stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D brain image registration. Unlike recent CNN-based registration approaches, such as VoxelMorph, which computes a registration field from a pair of 3D volumes using a single-stream network, we design a two-stream architecture able to estimate multi-level registration fields sequentially from a pair of feature pyramids. Our main contributions are: (i) we design a two-stream 3D encoder-decoder network that computes two convolutional feature pyramids separately from two input volumes; (ii) we propose sequential pyramid registration where a sequence of pyramid registration (PR) modules is designed to predict multi-level registration fields directly from the decoding feature pyramids. The registration fields are refined gradually in a coarse-to-fine manner via sequential warping, which equips the model with a strong capability for handling large deformations; (iii) the PR modules can be further enhanced by computing local 3D correlations between the feature pyramids, resulting in the improved Dual-PRNet able to aggregate rich detailed anatomical structure of the brain; (iv) our Dual-PRNet can be integrated into a 3D segmentation framework for joint registration and segmentation, by precisely warping voxel-level annotations. Our methods are evaluated on two standard benchmarks for brain MRI registration, where Dual-PRNet outperforms the state-of-the-art approaches by a large margin, i.e., improving recent VoxelMorph from 0.511 to 0.748 (Dice score) on the Mindboggle101 dataset. In addition, we further demonstrate that our methods can greatly facilitate the segmentation task in a joint learning framework, by leveraging limited annotations.
ArticleNumber	102379
Author	Scott, Matthew R. Hu, Xiaojun Reyes, Mauricio Huang, Weilin Kang, Miao
Author_xml	– sequence: 1 givenname: Miao surname: Kang fullname: Kang, Miao organization: Malong LLC, Wilmington, USA – sequence: 2 givenname: Xiaojun surname: Hu fullname: Hu, Xiaojun organization: Malong LLC, Wilmington, USA – sequence: 3 givenname: Weilin orcidid: 0000-0002-1520-4140 surname: Huang fullname: Huang, Weilin email: whuang@malongtech.com, whuang@robots.ox.ac.uk organization: Malong LLC, Wilmington, USA – sequence: 4 givenname: Matthew R. surname: Scott fullname: Scott, Matthew R. organization: Malong LLC, Wilmington, USA – sequence: 5 givenname: Mauricio orcidid: 0000-0002-2434-9990 surname: Reyes fullname: Reyes, Mauricio organization: ARTORG Center for Biomedical Engineering Research, Univ. of Bern, Switzerland
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/35349836$$D View this record in MEDLINE/PubMed
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Keywords	3D segmentation Brain MRI Encoder-decoder network Deformable registration Medical image registration
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Snippet	•We propose a Dual-stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D medical image registration.•We design a two-stream 3D... We propose a Dual-stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D brain image registration. Unlike recent CNN-based...
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SubjectTerms	3D segmentation Annotations Benchmarks Brain Brain MRI Coders Decoding Deformable registration Encoder-decoder network Encoders-Decoders Humans Image Processing, Computer-Assisted Image registration Image segmentation Imaging, Three-Dimensional Magnetic Resonance Imaging Medical image registration Modules Neural Networks, Computer Neuroimaging Pyramids Registration Tomography, X-Ray Computed Warping
Title	Dual-stream pyramid registration network
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