Automatic Landmark Estimation for Adolescent Idiopathic Scoliosis Assessment Using BoostNet

Adolescent Idiopathic Scoliosis (AIS) exhibits as an abnormal curvature of the spine in teens. Conventional radiographic assessment of scoliosis is unreliable due to the need for manual intervention from clinicians as well as high variability in images. Current methods for automatic scoliosis assessment are not robust due to reliance on segmentation or feature engineering. We propose a novel framework for automated landmark estimation for AIS assessment by leveraging the strength of our newly designed BoostNet, which creatively integrates the robust feature extraction capabilities of Convolutional Neural Networks (ConvNet) with statistical methodologies to adapt to the variability in X-ray images. In contrast to traditional ConvNets, our BoostNet introduces two novel concepts: (1) a BoostLayer for robust discriminatory feature embedding by removing outlier features, which essentially minimizes the intra-class variance of the feature space and (2) a spinal structured multi-output regression layer for compact modelling of landmark coordinate correlation. The BoostNet architecture estimates required spinal landmarks within a mean squared error (MSE) rate of 0.00068 in 431 crossvalidation images and 0.0046 in 50 test images, demonstrating its potential for robust automated scoliosis assessment in the clinical setting.