Accurate Automated Cobb Angles Estimation Using Multi-View Extrapolation Net

Accurate automated quantitative estimation of spinal curvature is an important task for the clinical evaluation and treatment planning of scoliosis.It solves the disadvantage of manual Cobb angle measurement(time-consuming and unreliable)which is the current clinical standard for scoliosis assessment.A couple of attempts have been made for automated Cobb angle estimation on single-view x-rays.However,it is very challenging to achieve a highly accurate automated estimation of Cobb angles because it is difficult to utilize the information of Anterior-posterior(AP)and Lateral(LAT)view x-rays efficiently.We therefore propose a novel Multi-View Extrapolation Net(MVE-Net)architecture that can provide an accurate automated framework for scoliosis estimation and error correction in multi view(both AP and LAT)x-rays.The proposed MVE-Net consists of three closely-linked components:(1)a joint-view net learning AP and LAT angles jointly based on landmarks learned from joint representation,(2)an independent-view net learning AP and LAT angles independently based on landmarks learned from unique independent feature of AP or LAT angles,and(3)an inter-error correction net learning a combination function adaptively to offset the errors of the first two nets for accurate angle estimation.Experimental results on 526 x-ray images show an impressive 7.81 Circular Mean Absolute Error(CMAE)in AP Cobb angle and 6.26 CMAE in LAT Cobb angle estimation,which demonstrates the MVE-Net's capability of performing accurate estimation of Cobb angles in multi-view x-rays.Our method therefore provides clinicians with a framework for efficient,accurate,and reliable estimation of spinal curvature for comprehensive scoliosis assessment.