Automatic spinal curvature measurement on ultrasound spine images using Faster R-CNN

TL;DR

This paper presents an automatic framework using Faster R-CNN to detect vertebral lamina and measure spinal curvature on ultrasound images, aiming to replace manual, time-consuming assessments with a reliable, automated method.

Contribution

The study introduces a novel fully automatic approach for spinal curvature measurement on ultrasound images using Faster R-CNN, improving efficiency and accuracy over manual methods.

Findings

Achieved 0.76 AP on test set for lamina detection

Mean Absolute Difference within clinical acceptance error

Correlation of 0.79 with radiographic Cobb angles

Abstract

Ultrasound spine imaging technique has been applied to the assessment of spine deformity. However, manual measurements of scoliotic angles on ultrasound images are time-consuming and heavily rely on raters experience. The objectives of this study are to construct a fully automatic framework based on Faster R-CNN for detecting vertebral lamina and to measure the fitting spinal curves from the detected lamina pairs. The framework consisted of two closely linked modules: 1) the lamina detector for identifying and locating each lamina pairs on ultrasound coronal images, and 2) the spinal curvature estimator for calculating the scoliotic angles based on the chain of detected lamina. Two hundred ultrasound images obtained from AIS patients were identified and used for the training and evaluation of the proposed method. The experimental results showed the 0.76 AP on the test set, and the Mean Absolute Difference (MAD) between automatic and manual measurement which was within the clinical acceptance error. Meanwhile the correlation between automatic measurement and Cobb angle from radiographs was 0.79. The results revealed that our proposed technique could provide accurate and reliable automatic curvature measurements on ultrasound spine images for spine deformities.