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Classification of vertebral compression fractures (VCF) having osteoporotic or neoplastic origin is fundamental to the planning of treatment. We developed a fracture classification system by acquiring quantitative morphologic and bone density determinants of fracture progression through the use of automated measurements from longitudinal studies. A total of 250 CT studies were acquired for the task, each having previously identified VCFs with osteoporosis or neoplasm. Thirty-six features or each identified VCF were computed and classified using a committee of support vector machines. Ten-fold cross validation on 695 identified fractured vertebrae showed classification accuracies of 0.812, 0.665, and 0.820 for the measured, longitudinal, and combined feature sets respectively.
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The precise and accurate segmentation of the vertebral column is essential in the diagnosis and treatment of various orthopedic, neurological, and oncological traumas and pathologies. Segmentation is especially challenging in the presence of pathology such as vertebral compression fractures. In this paper, we propose a method to produce segmentations for osteoporotic compression fractured vertebrae by applying a multi-atlas joint label fusion technique for clinical CT images. A total of 170 thoracic and lumbar vertebrae were evaluated using atlases from five patients with varying degrees of spinal degeneration. In an osteoporotic cohort of bundled atlases, registration provided an average Dice coefficient and mean absolute surface distance of 2.7$pm$4.5% and 0.32$pm$0.13mm for osteoporotic vertebrae, respectively, and 90.9$pm$3.0% and 0.36$pm$0.11mm for compression fractured vertebrae.
The presence of a vertebral compression fracture is highly indicative of osteoporosis and represents the single most robust predictor for development of a second osteoporotic fracture in the spine or elsewhere. Less than one third of vertebral compression fractures are diagnosed clinically. We present an automated method for detecting spine compression fractures in Computed Tomography (CT) scans. The algorithm is composed of three processes. First, the spinal column is segmented and sagittal patches are extracted. The patches are then binary classified using a Convolutional Neural Network (CNN). Finally a Recurrent Neural Network (RNN) is utilized to predict whether a vertebral fracture is present in the series of patches.
Vehicle safety systems have substantially decreased motor vehicle crash-related injuries and fatalities, but injuries to the lumbar spine still have been reported. Experimental and computational analyses of upright and, particularly, reclined occupants in frontal crashes have shown that the lumbar spine can be subjected to axial compression followed by combined compression-flexion loading. Lumbar spine failure tolerance in combined compression-flexion has not been widely explored in the literature. Therefore, the goal of this study was to measure the failure tolerance of the lumbar spine in combined compression and flexion. Forty 3-vertebra lumbar spine segments were pre-loaded with axial compression and then subjected to dynamic flexion bending until failure. Clinically relevant middle vertebra fractures were observed in twenty-one of the specimens, including compression and burst fractures. The remaining nineteen specimens experienced failure at the potting grip interface. Since specimen characteristics and pre-test axial load varied widely within the sample, failure forces (mean 3.4 kN, range 1.6-5.1 kN) and moments (mean 73 Nm, range 0-181 Nm) also varied widely. Tobit univariate regressions were performed to determine the relationship between censored failure tolerance and specimen sex, segment type (upper/lower), age, and cross-sectional area. Age, sex, and cross-sectional area significantly affected failure force and moment individually (p<0.0024). These data can be used to develop injury prediction tools for lumbar spine fractures and further research in future safety systems.
This paper presents a systematic study the effects of compression on hyperspectral pixel classification task. We use five dimensionality reduction methods -- PCA, KPCA, ICA, AE, and DAE -- to compress 301-dimensional hyperspectral pixels. Compressed pixels are subsequently used to perform pixel-based classifications. Pixel classification accuracies together with compression method, compression rates, and reconstruction errors provide a new lens to study the suitability of a compression method for the task of pixel-based classification. We use three high-resolution hyperspectral image datasets, representing three common landscape units (i.e. urban, transitional suburban, and forests) collected by the Remote Sensing and Spatial Ecosystem Modeling laboratory of the University of Toronto. We found that PCA, KPCA, and ICA post greater signal reconstruction capability; however, when compression rate is more than 90% those methods showed lower classification scores. AE and DAE methods post better classification accuracy at 95% compression rate, however decreasing again at 97%, suggesting a sweet-spot at the 95% mark. Our results demonstrate that the choice of a compression method with the compression rate are important considerations when designing a hyperspectral image classification pipeline.
A 10 MeV/c $mu^+$ beam was stopped in helium gas of a few mbar in a magnetic field of 5 T. The muon swarm has been efficiently compressed from a length of 16 cm down to a few mm along the magnetic field axis (longitudinal compression) using electrostatic fields. The simulation reproduces the low energy interactions of slow muons in helium gas. Phase space compression occurs on the order of microseconds, compatible with the muon lifetime of 2 $mu$s. This paves the way for preparation of a high quality muon beam.
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