A Landmark Estimation and Correction Network for Automated Measurement of Sagittal Spinal Parameters

Recently, deep learning for spinal measurement in scoliosis achieved huge success. However, we notice that existing methods suffer low performance on lateral X-rays because of severe occlusion. In this paper, we propose the automated Landmark Estimation and Correction Network (LEC-Net) based on a convolutional neural network (CNN) to estimate landmarks on lateral X-rays. The framework consists of two parts (1) a landmark estimation network (LEN) and (2) a landmark correction network (LCN). The LEN first estimates 68 landmarks of 17 vertebrae (12 thoracic vertebrae and 5 lumbar vertebrae) per image. These landmarks may include some failed points on the area with occlusion. Then the LCN estimates the clinical parameters by considering the spinal curvature described by 68 landmarks as a constraint. Extensive experiment results which test on 240 lateral Xrays demonstrate that our method improves the landmark estimation accuracy and achieves high performance of clinical parameters on Xrays with severe occlusion. Implementation code is available at https://github.com/xiaoyanermiemie/LEN-LCN.