Scoliosis is a condition defined by an abnormal spinal curvature. For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles. We propose an automated method for the estimation of Cobb angles from X-ray scans. First, the centerline of the spine was segmented using a cascade of two convolutional neural networks. After smoothing the centerline, Cobb angles were automatically estimated using the derivative of the centerline. We evaluated the results using the mean absolute error and the average symmetric mean absolute percentage error between the manual assessment by experts and the automated predictions. For optimization, we used 609 X-ray scans from the London Health Sciences Center, and for evaluation, we participated in the international challenge “Accurate Automated Spinal Curvature Estimation, MICCAI 2019” (100 scans). On the challenge’s test set, we obtained an average symmetric mean absolute percentage error of 22.96.

Additional Metadata
Persistent URL dx.doi.org/10.1007/978-3-030-39752-4_10, hdl.handle.net/1765/125335
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Citation
Dubost, F. (Florian), Collery, B. (Benjamin), Renaudier, A. (Antonin), Roc, A. (Axel), Posocco, N. (Nicolas), Niessen, W.J, & de Bruijne, M. (2020). Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). doi:10.1007/978-3-030-39752-4_10