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The aim of this study was to determine whether sheep shearers have clinically hypothesized adaptive postural and sagittal mobility parameters of the lumbar spine and pelvis. Sixty-four shearers and 64 non-shearers, matched by age and anthropometry and surveyed for present and previous low back pain, participated in a study to determine the effects of occupation on sagittal spinal motion and posture. Lumbar and hip mobility measurements were made with a geometric CAD analysis of lateral photographs using surface reflective markers. Sagittal range of motion demonstrated similar ranges of lumbar flexion between the two groups; however, there was a marked gain in hip flexion in the shearers as well as a marked loss of lumbar extension. The shearers also demonstrated a more lordotic lower lumbar curvature compensated by a flatter (less kyphotic) mid to lower thoracic region. Shearers appear to lose lumbar extension, gain hip flexion and develop an adaptive normal stance. This adaptation appears to be independent of previous or current back pain. Conversely, lumbar extension loss in non-shearers correlates with previous back injury. A stepwise linear regression of all participants indicated that the occupation is the predominant influence on motion and posture followed by age. The implications are one of structural adaptation in this occupational group that does not appear to be correlated with back pain.
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PMID:The influence of occupation on lumbar sagittal motion and posture. 1608

Lower back pain (LBP) is widely prevalent all over the world and more than 80% of the people suffer from LBP at some point of their lives. Moreover, a shortage of radiologists is the most pressing cause for the need of CAD (computer-aided diagnosis) systems. Automatic localization and labeling of intervertebral discs from lumbar MRI is the first step towards computer-aided diagnosis of lower back ailments. Subsequently, for diagnosis and characterization (quantification and localization) of abnormalities like disc herniation and stenosis, a completely automatic segmentation of intervertebral discs and the dural sac is extremely important. Contribution of this paper towards clinical CAD systems is two-fold. First, we propose a method to automatically detect all visible intervertebral discs in clinical sagittal MRI using heuristics and machine learning techniques. We provide a novel end-to-end framework that outputs a tight bounding box for each disc, instead of simply marking the centroid of discs, as has been the trend in the recent past. Second, we propose a method to simultaneously segment all the tissues (vertebrae, intervertebral disc, dural sac and background) in a lumbar sagittal MRI, using an auto-context approach instead of any explicit shape features or models. Past work tackles the lumbar segmentation problem on a tissue/organ basis, and which tend to perform poorly in clinical scans due to high variability in appearance. We, on the other hand, train a series of robust classifiers (random forests) using image features and sparsely sampled context features, which implicitly represent the shape and configuration of the image. Both these methods have been tested on a huge clinical dataset comprising of 212 cases and show very promising results for both disc detection (98% disc localization accuracy and 2.08mm mean deviation) and sagittal MRI segmentation (dice similarity indices of 0.87 and 0.84 for the dural sac and the inter-vertebral disc, respectively).
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PMID:Supervised methods for detection and segmentation of tissues in clinical lumbar MRI. 2474 6