A Multi-View Semi-supervised learning method for knee joint cartilage segmentation combining multiple feature descriptors and image modalities
Multi-atlas based segmentation techniques constitute an effective approach in the automatic segmentation of medical images. Existing methods usually rely on single spectral descriptors extracted from a specific imaging modality. In this paper, we propose the Multi-View Knee Cartilage Segmentation (M...
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Taylor & Francis Group
2024-12-01
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| Series: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21681163.2024.2332398 |
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| author | Christos G. Chadoulos Dimitrios E. Tsaopoulos Serafeim P. Moustakidis John B. Theocharis |
| author_facet | Christos G. Chadoulos Dimitrios E. Tsaopoulos Serafeim P. Moustakidis John B. Theocharis |
| author_sort | Christos G. Chadoulos |
| collection | DOAJ |
| description | Multi-atlas based segmentation techniques constitute an effective approach in the automatic segmentation of medical images. Existing methods usually rely on single spectral descriptors extracted from a specific imaging modality. In this paper, we propose the Multi-View Knee Cartilage Segmentation (MV-KCS) approach, for segmenting the knee joint articular cartilage from MR images. Operating under the Semi-supervised learning framework, MV-KCS leverages spectral content from multiple feature spaces by constructing sparse graphs for each view individually, and aggregating them via optimisation to obtain a common data graph. In We consider two multi-view scenarios: in the former case views correspond to multiple feature descriptors, while on the latter, the views correspond to multiple image modalities. We propose two effective labelling schemes, implementing label propagation from the atlas library to the target image. The proposed methodology is applied to the publicly available Osteoarthritis Initiative repository. We devise a comprehensive experimental design to validate different test cases, comparing single-feature vs multi-features, multi-features vs feature stacking and multi-features vs multi-modalities. Comparative results and statistical analysis reveal that the proposed MV-KCS provides enhanced performance ([Formula: see text]), outperforming a series of patch-based approaches, six recent state-of-the-art deep supervised models and three deep semi-supervised ones, in terms of both classification and volumetric measures. |
| format | Article |
| id | doaj-art-9f26fce1968d410d9b527f519b16f4b4 |
| institution | Kabale University |
| issn | 2168-1163 2168-1171 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
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| series | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| spelling | doaj-art-9f26fce1968d410d9b527f519b16f4b42024-11-29T10:29:55ZengTaylor & Francis GroupComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization2168-11632168-11712024-12-0112110.1080/21681163.2024.2332398A Multi-View Semi-supervised learning method for knee joint cartilage segmentation combining multiple feature descriptors and image modalitiesChristos G. Chadoulos0Dimitrios E. Tsaopoulos1Serafeim P. Moustakidis2John B. Theocharis3Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, GreeceInstitute for Bio-Economy and Agri-Technology, Centre for Research and Technology – Hellas, Volos, GreeceDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, GreeceDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, GreeceMulti-atlas based segmentation techniques constitute an effective approach in the automatic segmentation of medical images. Existing methods usually rely on single spectral descriptors extracted from a specific imaging modality. In this paper, we propose the Multi-View Knee Cartilage Segmentation (MV-KCS) approach, for segmenting the knee joint articular cartilage from MR images. Operating under the Semi-supervised learning framework, MV-KCS leverages spectral content from multiple feature spaces by constructing sparse graphs for each view individually, and aggregating them via optimisation to obtain a common data graph. In We consider two multi-view scenarios: in the former case views correspond to multiple feature descriptors, while on the latter, the views correspond to multiple image modalities. We propose two effective labelling schemes, implementing label propagation from the atlas library to the target image. The proposed methodology is applied to the publicly available Osteoarthritis Initiative repository. We devise a comprehensive experimental design to validate different test cases, comparing single-feature vs multi-features, multi-features vs feature stacking and multi-features vs multi-modalities. Comparative results and statistical analysis reveal that the proposed MV-KCS provides enhanced performance ([Formula: see text]), outperforming a series of patch-based approaches, six recent state-of-the-art deep supervised models and three deep semi-supervised ones, in terms of both classification and volumetric measures.https://www.tandfonline.com/doi/10.1080/21681163.2024.2332398Osteoarthritis (OA)magnetic resonance imaging (MRI) segmentationmulti-view learningmulti-atlas patch-basedsemi-supervised learning (SSL) |
| spellingShingle | Christos G. Chadoulos Dimitrios E. Tsaopoulos Serafeim P. Moustakidis John B. Theocharis A Multi-View Semi-supervised learning method for knee joint cartilage segmentation combining multiple feature descriptors and image modalities Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Osteoarthritis (OA) magnetic resonance imaging (MRI) segmentation multi-view learning multi-atlas patch-based semi-supervised learning (SSL) |
| title | A Multi-View Semi-supervised learning method for knee joint cartilage segmentation combining multiple feature descriptors and image modalities |
| title_full | A Multi-View Semi-supervised learning method for knee joint cartilage segmentation combining multiple feature descriptors and image modalities |
| title_fullStr | A Multi-View Semi-supervised learning method for knee joint cartilage segmentation combining multiple feature descriptors and image modalities |
| title_full_unstemmed | A Multi-View Semi-supervised learning method for knee joint cartilage segmentation combining multiple feature descriptors and image modalities |
| title_short | A Multi-View Semi-supervised learning method for knee joint cartilage segmentation combining multiple feature descriptors and image modalities |
| title_sort | multi view semi supervised learning method for knee joint cartilage segmentation combining multiple feature descriptors and image modalities |
| topic | Osteoarthritis (OA) magnetic resonance imaging (MRI) segmentation multi-view learning multi-atlas patch-based semi-supervised learning (SSL) |
| url | https://www.tandfonline.com/doi/10.1080/21681163.2024.2332398 |
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