  Scoliosis is a three-dimensional deformity of the spine, most often diagnosed
in childhood. It affects 2-3% of the population, which is approximately seven
million people in North America. Currently, the reference standard for
assessing scoliosis is based on the manual assignment of Cobb angles at the
site of the curvature center. This manual process is time consuming and
unreliable as it is affected by inter- and intra-observer variance. To overcome
these inaccuracies, machine learning (ML) methods can be used to automate the
Cobb angle measurement process. This paper proposes to address the Cobb angle
measurement task using YOLACT, an instance segmentation model. The proposed
method first segments the vertebrae in an X-Ray image using YOLACT, then it
tracks the important landmarks using the minimum bounding box approach. Lastly,
the extracted landmarks are used to calculate the corresponding Cobb angles.
The model achieved a Symmetric Mean Absolute Percentage Error (SMAPE) score of
10.76%, demonstrating the reliability of this process in both vertebra
localization and Cobb angle measurement.
