Graph cut-based segmentation for intervertebral disc in human MRI
We introduce an automated algorithm for the 2D segmentation of both healthy and degenerated lumbar intervertebral discs (IVD) from T2-weighted Turbo Spin Echo(TSE) sagittal spine Magnetic Resonance Images (MRIs). Our approach employs a fast algorithm addressing the s-t max-flow/min-cut problem, inco...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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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/10.1080/21681163.2025.2475992 |
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| author | Leena Silvoster R. Mathusoothan S. Kumar |
| author_facet | Leena Silvoster R. Mathusoothan S. Kumar |
| author_sort | Leena Silvoster |
| collection | DOAJ |
| description | We introduce an automated algorithm for the 2D segmentation of both healthy and degenerated lumbar intervertebral discs (IVD) from T2-weighted Turbo Spin Echo(TSE) sagittal spine Magnetic Resonance Images (MRIs). Our approach employs a fast algorithm addressing the s-t max-flow/min-cut problem, incorporating anatomical knowledge of soft tissues in the human body. In the initial phase, preprocessing is applied to the input image to eliminate intensity inhomogeneity and noise. A graph is then constructed from the image pixels, and seed points are automatically initialised using a growing bounding box. In the second phase, the method applies the s-t max-flow/min-cut algorithm to separate an IVD from the background. This method effectively detects degenerated and healthy IVDs by applying the s-t max-flow/min-cut algorithm within a directed graph. The polynomial time complexity of this approach enables the exploration of a globally optimal solution, eliminating the need for user interaction in seed point selection. Validation of the algorithm on a dataset of 15 patients demonstrates its efficient segmentation performance, achieving a Dice Similarity Coefficient (DSC) of 89%. |
| format | Article |
| id | doaj-art-a8ab3da7d7044769b3f3f90729d083e9 |
| institution | DOAJ |
| issn | 2168-1163 2168-1171 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| spelling | doaj-art-a8ab3da7d7044769b3f3f90729d083e92025-08-20T03:20:30ZengTaylor & Francis GroupComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization2168-11632168-11712025-12-0113110.1080/21681163.2025.2475992Graph cut-based segmentation for intervertebral disc in human MRILeena Silvoster0R. Mathusoothan S. Kumar1Department of Computer Science, College of Engineering Attingal, IndiaDepartment of Information Technology, Noorul Islam Centre for Higher Education, IndiaWe introduce an automated algorithm for the 2D segmentation of both healthy and degenerated lumbar intervertebral discs (IVD) from T2-weighted Turbo Spin Echo(TSE) sagittal spine Magnetic Resonance Images (MRIs). Our approach employs a fast algorithm addressing the s-t max-flow/min-cut problem, incorporating anatomical knowledge of soft tissues in the human body. In the initial phase, preprocessing is applied to the input image to eliminate intensity inhomogeneity and noise. A graph is then constructed from the image pixels, and seed points are automatically initialised using a growing bounding box. In the second phase, the method applies the s-t max-flow/min-cut algorithm to separate an IVD from the background. This method effectively detects degenerated and healthy IVDs by applying the s-t max-flow/min-cut algorithm within a directed graph. The polynomial time complexity of this approach enables the exploration of a globally optimal solution, eliminating the need for user interaction in seed point selection. Validation of the algorithm on a dataset of 15 patients demonstrates its efficient segmentation performance, achieving a Dice Similarity Coefficient (DSC) of 89%.https://www.tandfonline.com/doi/10.1080/21681163.2025.2475992Magnetic resonance imaginglumbar intervertebral discgraph-cutintervertebral disc degeneration |
| spellingShingle | Leena Silvoster R. Mathusoothan S. Kumar Graph cut-based segmentation for intervertebral disc in human MRI Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Magnetic resonance imaging lumbar intervertebral disc graph-cut intervertebral disc degeneration |
| title | Graph cut-based segmentation for intervertebral disc in human MRI |
| title_full | Graph cut-based segmentation for intervertebral disc in human MRI |
| title_fullStr | Graph cut-based segmentation for intervertebral disc in human MRI |
| title_full_unstemmed | Graph cut-based segmentation for intervertebral disc in human MRI |
| title_short | Graph cut-based segmentation for intervertebral disc in human MRI |
| title_sort | graph cut based segmentation for intervertebral disc in human mri |
| topic | Magnetic resonance imaging lumbar intervertebral disc graph-cut intervertebral disc degeneration |
| url | https://www.tandfonline.com/doi/10.1080/21681163.2025.2475992 |
| work_keys_str_mv | AT leenasilvoster graphcutbasedsegmentationforintervertebraldiscinhumanmri AT rmathusoothanskumar graphcutbasedsegmentationforintervertebraldiscinhumanmri |