Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentation
This addresses the time-consuming and unreliable manual process for scoliosis assessment, which affects millions of people, but is incremental as it applies an existing method to a specific medical task.
The paper tackled automating Cobb angle measurement for adolescent idiopathic scoliosis using an instance segmentation model, achieving a Symmetric Mean Absolute Percentage Error (SMAPE) score of 10.76%.
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.