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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21681163.2025.2475992 |
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| Summary: | 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%. |
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| ISSN: | 2168-1163 2168-1171 |