Morphometric analysis of the relationships between intervertebral disc and vertebral body heights: an anatomical and radiographic study of the human thoracic spine.

The main aim of this study was to provide anatomical data on the heights of the human intervertebral discs for all levels of the thoracic spine by direct and radiographic measurements. Additionally, the heights of the neighboring vertebral bodies were measured, and the prediction of the disc heights based only on the size of the vertebral bodies was investigated. The anterior (ADH), middle (MDH) and posterior heights (PDH) of the discs were measured directly and on radiographs of 72 spine segments from 30 donors (age 57.43 ± 11.27 years). The radiographic measurement error and the reliability of the measurements were calculated. Linear and non-linear regression analyses were employed for investigation of statistical correlations between the heights of the thoracic disc and vertebrae. Radiographic measurements displayed lower repeatability and were shorter than the anatomical ones (approximately 9% for ADH and 37% for PDH). The thickness of the discs varied from 4.5 to 7.2 mm, with the MDH approximately 22.7% greater. The disc heights showed good correlations with the vertebral body heights (R(2), 0.659-0.835, P-values < 0.005; anova), allowing the generation of 10 prediction equations. New data on thoracic disc morphometry were provided in this study. The generated set of regression equations could be used to predict thoracic disc heights from radiographic measurement of the vertebral body height posterior. For the creation of parameterized models of the human thoracic discs, the use of the prediction equations could eliminate the need for direct measurement on intervertebral discs. Moreover, the error produced by radiographic measurements could be reduced at least for the PDH.

Prediction of the human thoracic and lumbar articular facet joint morphometry from radiographic images.

The articular facet joints (AFJ) play an important role in the biomechanics of the spine. Although it is well known that some AFJ dimensions (e.g. facet height/width or facet angles) play a major role in spinal deformities such as scoliosis, little is known about statistical correlations between these dimensions and the size of the vertebral bodies. Such relations could allow patient-specific prediction of AFJ morphometry from a few dimensions measurable by X-ray. This would be of clinical interest and could also provide parameters for mathematical modeling of the spine. Our purpose in this study was to generate prediction equations for 20 parameters of the human thoracic and lumbar AFJ from T1 to L4 as a function of only one given parameter, the vertebral body height posterior (VBHP). Linear and nonlinear regression analyses were performed with published anatomical data, including linear and angular dimensions of the AFJ and vertebral body heights, to find the best functions to describe the correlations between these parameters. Third-order polynomial regressions, in contrast to the linear, exponential and logarithmic regressions, provided moderate to high correlations between the AFJ parameters and vertebral body heights; e.g. facet height superior and interfacet width (R², 0.605-0.880); facet height inferior, interfacet height and sagittal/transverse angle superior (R², 0.875-0.973). Different correlations were found for facet width and transverse angle inferior in the thoracic (R², 0.703-0.930) and lumbar (R², 0.457-0.892) regions. A set of 20 prediction equations for AFJ parameters was generated (P-values < 0.005, anova). Comparison of the AFJ predictions with experimental data indicated mean percent errors <13%, with the exception of the thoracolumbar junction (T12-L1). It was possible to establish useful predictions for human thoracic and lumbar AFJ dimensions based on the size of the vertebral bodies. The generated set of equations allows the prediction of 20 AFJ parameters per vertebral level from the measurement of the parameter VBHP, which is easily performed on lateral X-rays. As the vertebral body height is unique for each person and vertebral level, the predicted AFJ parameters are also specific to an individual. This approach could be used for parameterized patient-specific modeling of the spine to explore the clinically important mechanical roles of the articular facets in pathological conditions, such as scoliosis.

Prediction equations for human thoracic and lumbar vertebral morphometry.

Statistical correlations between anatomical dimensions of human vertebral structures have indicated a potential for the prediction of vertebral morphometry, which could be applied to the creation of simplified geometrical models of the spine excluding the need for preliminary processing of medical images. The aim of this study was to perform linear and nonlinear regressions with published anatomical data to generate prediction equations for 20 vertebral parameters of the human thoracic and lumbar spine as a function of only one given parameter that was measured by X-ray. Each parameter was considered individually as a potential predictor variable in terms of its correlation with all of the other parameters, together with the readiness with which lateral X-rays could be obtained. Based on this, the parameter vertebral body height posterior was chosen and the statistical analyses described here are related to this parameter. Our linear, exponential and logarithmic regressions provided significant predictions of anterior vertebral structures. However, third-order polynomial prediction equations allowed an improvement on these predictions (P-values < 0.001), e.g. endplates and spinal canal (R(2), 0.970-0.995) as well as pedicle heights and the spinous process (R(2), 0.811-0.882), in addition to a reasonable prediction of the posterior vertebral structures, which have shown a low or no correlation in previous studies, e.g. pedicle inclination and transverse process (R(2), 0.514-0.693) (anova). Comparisons of the theoretical predictions with two other sets of experimental data indicated that the predictions generally agree well with the experimental data. A time-efficient approach for obtaining anatomical data for the description of human thoracic and lumbar geometry was provided by this method, which requires the measurement of only one parameter per vertebra (vertebral body height posterior) from a lateral X-ray and the set of developed prediction equations. Vertebral models based on this type of parameterized geometry could be used in biomechanical studies that require geometry variation, such as in spinal deformations, including scoliosis.

Analysis of manual segmentation in paranasal CT images.

Manual segmentation is often used for evaluation of automatic or semi-automatic segmentation. The purpose of this paper is to describe the inter and intraindividual variability, the dubiety of manual segmentation as a gold standard and to find reasons for the discrepancy. We realized two experiments. In the first one ten ENT surgeons, ten medical students and one engineer outlined the right maxillary sinus and ethmoid sinuses manually on a standard CT dataset of a human head. In the second experiment two participants outlined maxillary sinus and ethmoid sinuses five times consecutively. Manual segmentation was accomplished with custom software using a line segmentation tool. The first experiment shows the interindividual variability of manual segmentation which is higher for ethmoidal sinuses than for maxillary sinuses. The variability can be caused by the level of experience, different interpretation of the CT data or different levels of accuracy. The second experiment shows intraindividual variability which is lower than interindividual variability. Most variances in both experiments appear during segmentation of ethmoidal sinuses and outlining hiatus semilunaris. Concerning the inter and intraindividual variances the segmentation result of one manual segmenter could not directly be used as gold standard for the evaluation of automatic segmentation algorithms.