Weakly-supervised convolutional neural networks for multimodal image registration
•A method to infer voxel-level correspondence from higher-level anatomical labels.•Efficient and fully-automated registration for MR and ultrasound prostate images.•Validation experiments with 108 pairs of labelled interventional patient images.•Open-source implementation. One of the fundamental cha...
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| Published in | Medical image analysis Vol. 49; pp. 1 - 13 |
|---|---|
| Main Authors | , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
01.10.2018
Elsevier BV |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1361-8415 1361-8423 1361-8423 |
| DOI | 10.1016/j.media.2018.07.002 |
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| Abstract | •A method to infer voxel-level correspondence from higher-level anatomical labels.•Efficient and fully-automated registration for MR and ultrasound prostate images.•Validation experiments with 108 pairs of labelled interventional patient images.•Open-source implementation.
One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks. The proposed end-to-end convolutional neural network approach aims to predict displacement fields to align multiple labelled corresponding structures for individual image pairs during the training, while only unlabelled image pairs are used as the network input for inference. We highlight the versatility of the proposed strategy, for training, utilising diverse types of anatomical labels, which need not to be identifiable over all training image pairs. At inference, the resulting 3D deformable image registration algorithm runs in real-time and is fully-automated without requiring any anatomical labels or initialisation. Several network architecture variants are compared for registering T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients. A median target registration error of 3.6 mm on landmark centroids and a median Dice of 0.87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.
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| AbstractList | One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks. The proposed end-to-end convolutional neural network approach aims to predict displacement fields to align multiple labelled corresponding structures for individual image pairs during the training, while only unlabelled image pairs are used as the network input for inference. We highlight the versatility of the proposed strategy, for training, utilising diverse types of anatomical labels, which need not to be identifiable over all training image pairs. At inference, the resulting 3D deformable image registration algorithm runs in real-time and is fully-automated without requiring any anatomical labels or initialisation. Several network architecture variants are compared for registering T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients. A median target registration error of 3.6 mm on landmark centroids and a median Dice of 0.87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels. One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks. The proposed end-to-end convolutional neural network approach aims to predict displacement fields to align multiple labelled corresponding structures for individual image pairs during the training, while only unlabelled image pairs are used as the network input for inference. We highlight the versatility of the proposed strategy, for training, utilising diverse types of anatomical labels, which need not to be identifiable over all training image pairs. At inference, the resulting 3D deformable image registration algorithm runs in real-time and is fully-automated without requiring any anatomical labels or initialisation. Several network architecture variants are compared for registering T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients. A median target registration error of 3.6 mm on landmark centroids and a median Dice of 0.87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels. •A method to infer voxel-level correspondence from higher-level anatomical labels.•Efficient and fully-automated registration for MR and ultrasound prostate images.•Validation experiments with 108 pairs of labelled interventional patient images.•Open-source implementation. One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks. The proposed end-to-end convolutional neural network approach aims to predict displacement fields to align multiple labelled corresponding structures for individual image pairs during the training, while only unlabelled image pairs are used as the network input for inference. We highlight the versatility of the proposed strategy, for training, utilising diverse types of anatomical labels, which need not to be identifiable over all training image pairs. At inference, the resulting 3D deformable image registration algorithm runs in real-time and is fully-automated without requiring any anatomical labels or initialisation. Several network architecture variants are compared for registering T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients. A median target registration error of 3.6 mm on landmark centroids and a median Dice of 0.87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels. [Display omitted] One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks. The proposed end-to-end convolutional neural network approach aims to predict displacement fields to align multiple labelled corresponding structures for individual image pairs during the training, while only unlabelled image pairs are used as the network input for inference. We highlight the versatility of the proposed strategy, for training, utilising diverse types of anatomical labels, which need not to be identifiable over all training image pairs. At inference, the resulting 3D deformable image registration algorithm runs in real-time and is fully-automated without requiring any anatomical labels or initialisation. Several network architecture variants are compared for registering T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients. A median target registration error of 3.6 mm on landmark centroids and a median Dice of 0.87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks. The proposed end-to-end convolutional neural network approach aims to predict displacement fields to align multiple labelled corresponding structures for individual image pairs during the training, while only unlabelled image pairs are used as the network input for inference. We highlight the versatility of the proposed strategy, for training, utilising diverse types of anatomical labels, which need not to be identifiable over all training image pairs. At inference, the resulting 3D deformable image registration algorithm runs in real-time and is fully-automated without requiring any anatomical labels or initialisation. Several network architecture variants are compared for registering T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients. A median target registration error of 3.6 mm on landmark centroids and a median Dice of 0.87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels. |
| Author | Bonmati, Ester Barratt, Dean C. Vercauteren, Tom Ourselin, Sébastien Noble, J. Alison Bandula, Steven Modat, Marc Ghavami, Nooshin Moore, Caroline M. Li, Wenqi Wang, Guotai Gibson, Eli Hu, Yipeng Emberton, Mark |
| AuthorAffiliation | a Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK c Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK e Division of Surgery and Interventional Science, University College London, London, UK d Centre for Medical Imaging, University College London, London, UK b Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK |
| AuthorAffiliation_xml | – name: e Division of Surgery and Interventional Science, University College London, London, UK – name: d Centre for Medical Imaging, University College London, London, UK – name: a Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK – name: b Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK – name: c Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK |
| Author_xml | – sequence: 1 givenname: Yipeng orcidid: 0000-0003-4902-0486 surname: Hu fullname: Hu, Yipeng email: yipeng.hu@ucl.ac.uk organization: Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK – sequence: 2 givenname: Marc orcidid: 0000-0002-5277-8530 surname: Modat fullname: Modat, Marc organization: Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK – sequence: 3 givenname: Eli orcidid: 0000-0001-9207-7280 surname: Gibson fullname: Gibson, Eli organization: Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK – sequence: 4 givenname: Wenqi orcidid: 0000-0002-7432-7386 surname: Li fullname: Li, Wenqi organization: Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK – sequence: 5 givenname: Nooshin surname: Ghavami fullname: Ghavami, Nooshin organization: Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK – sequence: 6 givenname: Ester surname: Bonmati fullname: Bonmati, Ester organization: Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK – sequence: 7 givenname: Guotai orcidid: 0000-0002-8632-158X surname: Wang fullname: Wang, Guotai organization: Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK – sequence: 8 givenname: Steven surname: Bandula fullname: Bandula, Steven organization: Centre for Medical Imaging, University College London, London, UK – sequence: 9 givenname: Caroline M. surname: Moore fullname: Moore, Caroline M. organization: Division of Surgery and Interventional Science, University College London, London, UK – sequence: 10 givenname: Mark orcidid: 0000-0003-4230-0338 surname: Emberton fullname: Emberton, Mark organization: Division of Surgery and Interventional Science, University College London, London, UK – sequence: 11 givenname: Sébastien surname: Ourselin fullname: Ourselin, Sébastien organization: Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK – sequence: 12 givenname: J. Alison orcidid: 0000-0002-3060-3772 surname: Noble fullname: Noble, J. Alison organization: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK – sequence: 13 givenname: Dean C. surname: Barratt fullname: Barratt, Dean C. organization: Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK – sequence: 14 givenname: Tom orcidid: 0000-0003-1794-0456 surname: Vercauteren fullname: Vercauteren, Tom organization: Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30007253$$D View this record in MEDLINE/PubMed |
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| Snippet | •A method to infer voxel-level correspondence from higher-level anatomical labels.•Efficient and fully-automated registration for MR and ultrasound prostate... One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence.... |
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| SubjectTerms | 3-D printers Artificial neural networks Centroids Convolutional neural network Deformation Formability Genetic transformation Glands Image registration Image-guided intervention Inference Labels Magnetic resonance imaging Medical image registration Medical imaging Neural networks NMR Nuclear magnetic resonance Organs Patients Prostate cancer Training Ultrasound Weakly-supervised learning |
| Title | Weakly-supervised convolutional neural networks for multimodal image registration |
| URI | https://dx.doi.org/10.1016/j.media.2018.07.002 https://www.ncbi.nlm.nih.gov/pubmed/30007253 https://www.proquest.com/docview/2126555326 https://www.proquest.com/docview/2070250547 https://pubmed.ncbi.nlm.nih.gov/PMC6742510 |
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