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Fat infiltration and atrophy of lumbar muscles are related to spinal degenerative conditions and may cause functional deficits. Spinal alignment exerts biomechanical influence on lumbar intervertebral discs and joints. Our objective was to evaluate if spinopelvic parameters correlate with the lumbar muscle volume and fat infiltration. This is an observational, prospective and cross-sectional study. Ninety-three asymptomatic adult aged 20–40 years were included. Lumbar lordosis (LL), thoracic kyphosis (TK), pelvic incidence (PI), pelvic tilt (PT), sacral slope (SS), thoracolumbar alignment (TL), sagittal vertical axis (SVA), C2-pelvic angle (CPA), spinosacral angle (SSA), lack of lordosis (PI-LL), L1S1 and T1S1 length were measured on panoramic spine radiographs. Lumbar axial T1-weighted and In- and Out-Phase images were obtained on 1.5T MRI scanner and were used to extract the muscle volumes and fat fractions of multifidus, erector spinae, and psoas. All muscle volumes were higher in men than women (p<0.05). The fat fraction was higher in the multifidus and erector spinae in women (p<0.05). Multifidus volume was weakly correlated with PT (R = 0.22), PI (R = 0.22), LL (R = 0.34) and CPA (R = 0.29). Erectors spinae volume were correlated with CPA (R = 0.21). Psoas volume correlated with TK (R = 0.21), TL (R = 0.27) and SVA (R = -0.23). The lumbar muscle volumes showed a moderated correlation with T1S1 length (R = 0.55 to 0.62). Spinopelvic parameters showed correlation with lumbar muscle volumes but not with muscle fat infiltration on asymptomatic young adults. [ABSTRACT FROM AUTHOR]

Study Design: Cross-sectional observational study on the relationship between the degrees of disc degeneration and sagittal alignment in asymptomatic healthy individuals.Objective: This study sought to determine whether the sagittal spine alignment subtype is related to the prevalence of lumbar disc degeneration.Summary and Background Data: Sagittal balance and spinopelvic parameters might be risk factors for disc degeneration.Methods: A total of 70 asymptomatic participants (36 women and 34 men) without regular physical activity were categorized according to the four subtypes of sagittal alignment proposed by Roussouly. All participants underwent magnetic resonance imaging of the lumbar spine (1.5T) and panoramic radiography of the spine. The degree of disc degeneration was graded using T2-weighted images according to the Pfirrmann classification. Spinopelvic parameters and vertebral curvatures were measured on digital panoramic radiographs using Surgimap software. Interobserver analyses for the Pfirrmann classification and spinopelvic parameters were assessed using the weighted Kappa and intraclass correlation coefficient (ICC), respectively.Results: The Kappa associated with disc degeneration classification was 0.79 (95% confidence intervals 0.72-0.87). The ICCs were excellent, with small confidence intervals for all spinopelvic parameters. The type II group (flat lordosis) showed a higher frequency of degenerated discs at L4-L5 (P = 0.03) than the type IV group (long and curved lumbar spine). No significant differences in disc degeneration were observed among the four subtypes at the other disc levels. We found a negative, moderate correlation between the spinopelvic parameters and the occurrence of disc degeneration in the type II group.Conclusion: The Roussouly subtype II sagittal alignment is significantly associated with disc degeneration at L4-L5 in asymptomatic young adults. Our results support the hypothesis that spinal sagittal alignment plays a role in early disc degeneration.Level Of Evidence: 3. [ABSTRACT FROM AUTHOR]

Accurate and efficient segmenting of vertebral bodies, muscles, and discs is crucial for analyzing various spinal diseases. However, traditional methods are either laborious and time-consuming (manual segmentation) or require extensive training data (fully automatic segmentation). FastCleverSeg, our proposed semi-automatic segmentation approach, addresses those limitations by significantly reducing user interaction while maintaining high accuracy. First, we reduce user interaction by requiring the manual annotation of only two or three slices. Next, we automatically Estimate the Annotation on Intermediary Slices (EANIS) using traditional computer vision/graphics concepts. Finally, our proposed method leverages improved voxel weight balancing to achieve fast and precise volumetric segmentation in the segmentation process. Experimental evaluations on our assembled diverse MRI databases comprising 179 patients (60 male, 119 female), demonstrate a remarkable 25 ms (30 ms standard deviation) processing time and a significant reduction in user interaction compared to existing approaches. Importantly, FastCleverSeg maintains or surpasses the segmentation quality of competing methods, achieving a Dice score of 94%. This invaluable tool empowers physicians to efficiently generate reliable ground truths, expediting the segmentation process and paving the way for future integration with deep learning approaches. In turn, this opens exciting possibilities for future fully automated spine segmentation. [ABSTRACT FROM AUTHOR]