Mr. Charles Bergeron Profile

Charles Bergeron

at Ecole Polytechnique de Montréal

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Proceedings Article | 12 April 2005 Paper

Robust prediction of three-dimensional spinal curve from back surface for non-invasive follow-up of scoliosis

Charles Bergeron, Hubert Labelle, Janet Ronsky, Ronald Zernicke

Proceedings Volume 5744, (2005) https://doi.org/10.1117/12.595655

KEYWORDS: X-rays, Signal to noise ratio, Spine, Data acquisition, 3D acquisition, Principal component analysis, Data analysis, Databases, Artificial intelligence, 3D image processing

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Spinal curvature progression in scoliosis patients is monitored from X-rays, and this serial exposure to harmful radiation increases the incidence of developing cancer. With the aim of reducing the invasiveness of follow-up, this study seeks to relate the three-dimensional external surface to the internal geometry, having assumed that that the physiological links between these are sufficiently regular across patients. A database was used of 194 quasi-simultaneous acquisitions of two X-rays and a 3D laser scan of the entire trunk. Data was processed to sets of datapoints representing the trunk surface and spinal curve. Functional data analyses were performed using generalized Fourier series using a Haar basis and functional minimum noise fractions. The resulting coefficients became inputs and outputs, respectively, to an array of support vector regression (SVR) machines. SVR parameters were set based on theoretical results, and cross-validation increased confidence in the system's performance. Predicted lateral and frontal views of the spinal curve from the back surface demonstrated average L₂-errors of 6.13 and 4.38 millimetres, respectively, across the test set; these compared favourably with measurement error in data. This constitutes a first robust prediction of the 3D spinal curve from external data using learning techniques.

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