Neurospine.
2024 Jun;21(2):665-675. 10.14245/ns.2448060.030.
The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images
- Liang YW
1 - Fang YT1,2
- Lin TC1,3
- Yang CR1,4
- Chang CC1,5,6
- Chang HK5,6
- Ko CC5,6
- Tu TH5,6
- Fay LY5,6
- Wu JC5,6
- Huang WC5,6
- Hu HW7
- Chen YY1,8
- Kuo CH1,5,6
- Affiliations
-
- 1Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan
- 2Department of Biomedical Engineering, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu County, Taiwan
- 3RadiRad Co., Ltd., New Taipei City, Taiwan
- 4Department of Artificial Intelligence in Healthcare, International Academia of Biomedical Innovation Technology, Reno, NV, USA
- 5Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- 6School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- 7Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu County, Taiwan
- 8Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, New Taipei City, Taiwan
- KMID: 2557039
- DOI: http://doi.org/10.14245/ns.2448060.030
Abstract
Objective
This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans.
Methods
Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness.
Results
The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements.
Conclusion
Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.
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