Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks

TL;DR

This paper presents an automated method using cascaded neural networks to accurately estimate spinal curvature from X-ray scans, aiding scoliosis diagnosis and treatment planning.

Contribution

It introduces a novel neural network cascade for spine centerline extraction and automated Cobb angle estimation from X-ray images.

Findings

Achieved a 22.96% symmetric mean absolute percentage error on the MICCAI 2019 challenge.

Demonstrated effective spine centerline segmentation with neural networks.

Provided a fully automated pipeline for scoliosis assessment.

Abstract

Scoliosis is a condition defined by an abnormal spinal curvature. For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles. We propose an automated method for the estimation of Cobb angles from X-ray scans. First, the centerline of the spine was segmented using a cascade of two convolutional neural networks. After smoothing the centerline, Cobb angles were automatically estimated using the derivative of the centerline. We evaluated the results using the mean absolute error and the average symmetric mean absolute percentage error between the manual assessment by experts and the automated predictions. For optimization, we used 609 X-ray scans from the London Health Sciences Center, and for evaluation, we participated in the international challenge "Accurate Automated Spinal Curvature Estimation, MICCAI 2019" (100 scans). On the challenge's test set, we obtained an average symmetric mean absolute percentage error of 22.96.