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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Methods Consecutive patients with lumbar spondylosis or low-grade spondylolisthesis who underwent 1- or 2-level DS between L3–5 with a minimum follow-up of 24 months were reviewed. Screw loosening was determined by computed tomography and the incidences were compared.
Results A total of 291 patients who underwent Dynesys DS (235 pedicle- and 56 CBT-based, respectively) were compared. The demographics and preoperative conditions were similar. All the clinical outcomes improved at 24-month postoperation, while the CBT-based group had less operation time and blood loss than the pedicle-based group. The rates of screw loosening were lower in the CBT-based (5.4% per screw and 12.5% per patient) than the pedicle-based group (9% per screw and 26.4% per patient). Furthermore, there were no differences in the clinical outcomes and complication profiles.
Conclusion The CBT-based DS for 1- or 2-level lumbar degeneration demonstrated equivalent clinical improvement as the pedicle-based DS. The adaption of CBT-based screws for DS could be a less invasive approach (shorter operation time and less blood loss), with lower chances of screw loosening than the conventional PS-based DS.
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Methods Consecutive patients who underwent surgery for CVJ disease were included for comparison to an age- and sex-matched cohort of normal CVJ persons as controls. The demographics, perioperative information, and pre- and postoperative 2-year cervical MRIs were collected for analysis. Cervical TAs were measured and compared.
Results A total of 201 patients, all of whom had pre- or postoperative MRI, were analyzed. The TA of the CVJ deformity group was larger than the healthy control group (1.62 ± 0.57 cm2 vs. 1.01 ± 0.18 cm2, p < 0.001). Moreover, patients who had combined anterior odontoidectomy and posterior laminectomy with fixation had the greatest reduction in the TA (1.18 ± 0.58 cm2).
Conclusion In CVJ deformity, the measurement of the cervical TA could indicate the severity of brain stem compression. After surgery, the TA had a varying degree of improvement, which could represent the efficacy of surgery.
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