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Scoliosis is a condition defined by an abnormal spinal curvature. Fordiagnosis and treatment planning of scoliosis, spinal curvature can beestimated using Cobb angles. We propose an automated method for the estimationof Cobb angles from X-ray scans. First, the centerline of the spine wassegmented using a cascade of two convolutional neural networks. After smoothingthe centerline, Cobb angles were automatically estimated using the derivativeof the centerline. We evaluated the results using the mean absolute error andthe average symmetric mean absolute percentage error between the manualassessment by experts and the automated predictions. For optimization, we used609 X-ray scans from the London Health Sciences Center, and for evaluation, weparticipated in the international challenge "Accurate Automated SpinalCurvature Estimation, MICCAI 2019" (100 scans). On the challenge's test set, weobtained an average symmetric mean absolute percentage error of 22.96.

Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks