Automatic spinal curvature measurement on ultrasound spine images using Faster R-CNN
This provides an accurate and reliable automatic method for measuring spinal curvature in spine deformities, reducing reliance on manual expertise, though it is incremental as it applies an existing method to a new domain.
The study tackled the problem of time-consuming and experience-dependent manual measurements of scoliotic angles on ultrasound spine images by developing a fully automatic framework using Faster R-CNN, achieving a 0.76 AP on the test set and a correlation of 0.79 with manual measurements.
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.