Abstract
Current classification systems for adolescent idiopathic scoliosis lack information on how the spine is deformed in three dimensions (3D), which can mislead further treatment recommendations. We propose an approach to address this issue by a deep learning method for the classification of 3D spine reconstructions of patients. A low-dimensional manifold representation of the spine models was learnt by stacked auto-encoders. A K-Means++ algorithm using a probabilistic seeding method clustered the low-dimensional codes to discover sub-groups in the studied population. We evaluated the method with a case series analysis of 155 patients with Lenke Type-1 thoracic spinal deformations recruited at our institution. The clustering algorithm proposed 5 sub-groups from the thoracic population, yielding statistically significant differences in clinical geometric indices between all clusters. These results demonstrate the presence of 3D variability within a pre-defined 2D group of spinal deformities.
Supported by the CHU Sainte-Justine Academic Research Chair in Spinal Deformities, the Canada Research Chair in Medical Imaging and Assisted Interventions, the 3D committee of the Scoliosis Research Society, the Natural Sciences and Engineering Research Council of Canada and the MEDITIS program.
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Thong, W.E., Labelle, H., Shen, J., Parent, S., Kadoury, S. (2015). Stacked Auto-encoders for Classification of 3D Spine Models in Adolescent Idiopathic Scoliosis. In: Yao, J., Glocker, B., Klinder, T., Li, S. (eds) Recent Advances in Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-14148-0_2
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