Pioneering precision in lumbar spine MRI segmentation with advanced deep learning and data enhancement
This study presents an advanced approach to lumbar spine segmentation using deep learning techniques, focusing on addressing key challenges such as class imbalance and data preprocessing. Magnetic resonance imaging (MRI) scans of patients with low back pain are meticulously preprocessed to accuratel...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Machine Learning with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827025000180 |
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| author | Istiak Ahmed Md. Tanzim Hossain Md. Zahirul Islam Nahid Kazi Shahriar Sanjid Md. Shakib Shahariar Junayed M. Monir Uddin Mohammad Monirujjaman Khan |
| author_facet | Istiak Ahmed Md. Tanzim Hossain Md. Zahirul Islam Nahid Kazi Shahriar Sanjid Md. Shakib Shahariar Junayed M. Monir Uddin Mohammad Monirujjaman Khan |
| author_sort | Istiak Ahmed |
| collection | DOAJ |
| description | This study presents an advanced approach to lumbar spine segmentation using deep learning techniques, focusing on addressing key challenges such as class imbalance and data preprocessing. Magnetic resonance imaging (MRI) scans of patients with low back pain are meticulously preprocessed to accurately represent three critical classes: vertebrae, spinal canal, and intervertebral discs (IVDs). By rectifying class inconsistencies in the data preprocessing stage, the fidelity of the training data is ensured. The modified U-Net model incorporates innovative architectural enhancements, including an upsample block with leaky Rectified Linear Units (ReLU) and Glorot uniform initializer, to mitigate common issues such as the dying ReLU problem and improve stability during training. Introducing a custom combined loss function effectively tackles class imbalance, significantly improving segmentation accuracy. Evaluation using a comprehensive suite of metrics showcases the superior performance of this approach, outperforming existing methods and advancing the current techniques in lumbar spine segmentation. These findings hold significant advancements for enhanced lumbar spine MRI and segmentation diagnostic accuracy. |
| format | Article |
| id | doaj-art-74a6c18316bc45709c3f5b5ac2048ffd |
| institution | DOAJ |
| issn | 2666-8270 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj-art-74a6c18316bc45709c3f5b5ac2048ffd2025-08-20T03:21:01ZengElsevierMachine Learning with Applications2666-82702025-06-012010063510.1016/j.mlwa.2025.100635Pioneering precision in lumbar spine MRI segmentation with advanced deep learning and data enhancementIstiak Ahmed0Md. Tanzim Hossain1Md. Zahirul Islam Nahid2Kazi Shahriar Sanjid3Md. Shakib Shahariar Junayed4M. Monir Uddin5Mohammad Monirujjaman Khan6Department of Electrical & Computer Engineering, North South University, Dhaka 1229, BangladeshDepartment of Computer Science, Friedrich-Alexander University, Erlangen, Nürnberg, 91054, GermanyDepartment of Electrical & Computer Engineering, North South University, Dhaka 1229, BangladeshDepartment of Electrical & Computer Engineering, North South University, Dhaka 1229, BangladeshDepartment of Electrical & Computer Engineering, North South University, Dhaka 1229, BangladeshDepartment of Mathematics and Physics, North South University, Dhaka 1229, Bangladesh; Corresponding author.Department of Electrical & Computer Engineering, North South University, Dhaka 1229, BangladeshThis study presents an advanced approach to lumbar spine segmentation using deep learning techniques, focusing on addressing key challenges such as class imbalance and data preprocessing. Magnetic resonance imaging (MRI) scans of patients with low back pain are meticulously preprocessed to accurately represent three critical classes: vertebrae, spinal canal, and intervertebral discs (IVDs). By rectifying class inconsistencies in the data preprocessing stage, the fidelity of the training data is ensured. The modified U-Net model incorporates innovative architectural enhancements, including an upsample block with leaky Rectified Linear Units (ReLU) and Glorot uniform initializer, to mitigate common issues such as the dying ReLU problem and improve stability during training. Introducing a custom combined loss function effectively tackles class imbalance, significantly improving segmentation accuracy. Evaluation using a comprehensive suite of metrics showcases the superior performance of this approach, outperforming existing methods and advancing the current techniques in lumbar spine segmentation. These findings hold significant advancements for enhanced lumbar spine MRI and segmentation diagnostic accuracy.http://www.sciencedirect.com/science/article/pii/S2666827025000180Lumbar spineDeep learningSegmentationClass imbalanceMedical image analysis |
| spellingShingle | Istiak Ahmed Md. Tanzim Hossain Md. Zahirul Islam Nahid Kazi Shahriar Sanjid Md. Shakib Shahariar Junayed M. Monir Uddin Mohammad Monirujjaman Khan Pioneering precision in lumbar spine MRI segmentation with advanced deep learning and data enhancement Machine Learning with Applications Lumbar spine Deep learning Segmentation Class imbalance Medical image analysis |
| title | Pioneering precision in lumbar spine MRI segmentation with advanced deep learning and data enhancement |
| title_full | Pioneering precision in lumbar spine MRI segmentation with advanced deep learning and data enhancement |
| title_fullStr | Pioneering precision in lumbar spine MRI segmentation with advanced deep learning and data enhancement |
| title_full_unstemmed | Pioneering precision in lumbar spine MRI segmentation with advanced deep learning and data enhancement |
| title_short | Pioneering precision in lumbar spine MRI segmentation with advanced deep learning and data enhancement |
| title_sort | pioneering precision in lumbar spine mri segmentation with advanced deep learning and data enhancement |
| topic | Lumbar spine Deep learning Segmentation Class imbalance Medical image analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2666827025000180 |
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