Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach
Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. I...
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| Published in: | IEEE transactions on medical imaging Vol. 38; no. 9; pp. 2151 - 2164 |
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| Main Authors: | , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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United States
IEEE
01.09.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0278-0062, 1558-254X, 1558-254X |
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| Abstract | Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localization tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, a refinement step is designed to explicitly impose shape prior knowledge and improve segmentation quality. This step is effective for overcoming image artifacts (e.g., due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialize atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution, and anatomically smooth bi-ventricular 3D models, despite the presence of artifacts in input CMR volumes. |
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| AbstractList | Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, a refinement step is designed to explicitly impose shape prior knowledge and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The pipeline is fully automated, due to network’s ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the presence of artefacts in input CMR volumes. Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localization tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, a refinement step is designed to explicitly impose shape prior knowledge and improve segmentation quality. This step is effective for overcoming image artifacts (e.g., due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The pipeline is fully automated, due to network’s ability to infer landmarks, which are then used downstream in the pipeline to initialize atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution, and anatomically smooth bi-ventricular 3D models, despite the presence of artifacts in input CMR volumes. Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localization tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, a refinement step is designed to explicitly impose shape prior knowledge and improve segmentation quality. This step is effective for overcoming image artifacts (e.g., due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialize atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution, and anatomically smooth bi-ventricular 3D models, despite the presence of artifacts in input CMR volumes.Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localization tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, a refinement step is designed to explicitly impose shape prior knowledge and improve segmentation quality. This step is effective for overcoming image artifacts (e.g., due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialize atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution, and anatomically smooth bi-ventricular 3D models, despite the presence of artifacts in input CMR volumes. |
| Author | Biffi, Carlo Duan, Jinming Schlemper, Jo de Marvao, Antonio Doumoud, Georgia Rueckert, Daniel Bello, Ghalib Dawes, Timothy J. W. Bai, Wenjia O'Regan, Declan P. |
| Author_xml | – sequence: 1 givenname: Jinming orcidid: 0000-0002-5108-2128 surname: Duan fullname: Duan, Jinming email: j.duan@imperial.ac.uk organization: Biomedical Image Analysis Group, Imperial College London, London, U.K – sequence: 2 givenname: Ghalib orcidid: 0000-0002-7893-743X surname: Bello fullname: Bello, Ghalib organization: MRC London Institute of Medical Sciences, Imperial College London, London, U.K – sequence: 3 givenname: Jo orcidid: 0000-0003-1867-1155 surname: Schlemper fullname: Schlemper, Jo organization: Biomedical Image Analysis Group, Imperial College London, London, U.K – sequence: 4 givenname: Wenjia orcidid: 0000-0003-2943-7698 surname: Bai fullname: Bai, Wenjia organization: Biomedical Image Analysis Group, Imperial College London, London, U.K – sequence: 5 givenname: Timothy J. W. orcidid: 0000-0001-7871-524X surname: Dawes fullname: Dawes, Timothy J. W. organization: MRC London Institute of Medical Sciences, Imperial College London, London, U.K – sequence: 6 givenname: Carlo surname: Biffi fullname: Biffi, Carlo organization: Biomedical Image Analysis Group, Imperial College London, London, U.K – sequence: 7 givenname: Antonio orcidid: 0000-0001-9095-5887 surname: de Marvao fullname: de Marvao, Antonio organization: MRC London Institute of Medical Sciences, Imperial College London, London, U.K – sequence: 8 givenname: Georgia surname: Doumoud fullname: Doumoud, Georgia organization: MRC London Institute of Medical Sciences, Imperial College London, London, U.K – sequence: 9 givenname: Declan P. orcidid: 0000-0002-0691-0270 surname: O'Regan fullname: O'Regan, Declan P. organization: MRC London Institute of Medical Sciences, Imperial College London, London, U.K – sequence: 10 givenname: Daniel orcidid: 0000-0002-5683-5889 surname: Rueckert fullname: Rueckert, Daniel organization: Biomedical Image Analysis Group, Imperial College London, London, U.K |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30676949$$D View this record in MEDLINE/PubMed |
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| Snippet | Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have... |
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| SubjectTerms | Algorithms Automation bi-ventricular CMR segmentation cardiac artifacts Cardiac Imaging Techniques - methods Computer applications Deep Learning Heart Heart - diagnostic imaging Humans Hypertension Image processing Image segmentation Imaging Imaging, Three-Dimensional - methods label fusion landmark localization Localization Machine learning Magnetic resonance Magnetic resonance imaging Magnetic Resonance Imaging, Cine - methods multi-atlas segmentation non-rigid registration Pipelines Propagation Robustness (mathematics) Shape shape prior Three dimensional models Three-dimensional displays Ventricle |
| Title | Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach |
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