Automatic Spine Curvature Estimation from X-ray Images

Automatic Spine Curvature Estimation from X-ray Images

Team: Bishesh Khanal, Bidur Khanal, Arnav Chavan (remote intern, IIT Dhanbad, India), Risav Tiwari (remote intern, IIT Dhanbad, India), Aryan Raj (remote intern, IIT Dhanbad, India)

Idiopathic scoliosis is one of the most common spinal deformities that can be potentially lethal if not intervened early enough. Cobb angle that provides a measurement for the spinal deformation curvature is a clinical standard for diagnosing scoliosis and an important metric for treatment planning. However, cobb angle measurement suffers from inter-operator variability and is tedious to measure for surgeons or radiologists.

Source: Wu, H. , Bailey, C. , Rasoulinejad, P. , & Li, S.. Automatic Landmark Estimation for Adolescent Idiopathic Scoliosis Assessment Using BoostNet. MICCAI 2017.

We are developing an automated method for measuring cobb angle directly from X-ray images of spine exploring robust methods that can adapt to test set images that come from a different distribution than training set images.

Relevant publications and links:

  1. Khanal, B., Dahal, L., Adhikari, P., & Khanal, B. (2019, October). Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regression. In International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging (pp. 81-87). Springer, Cham. pdf
Research Themes: Transforming Global health with AI (TOGAI)
Project Category: Computer Vision, Machine Learning, Medical Imaging