A deep-learning system for automatic detection of osteoporotic vertebral compression fractures at thoracolumbar junction using low-dose computed tomography images

Abstract

Abstract Purpose: To develop a deep-learning system for automatic osteoporotic vertebral compression fractures (OVCF) detection at the thoracolumbar junction using low-dose computed tomography (CT) images. Materials and methods: 500 individuals were enrolled in this retrospective study, including 270 normal and 230 OVCF cases. The cases were divided into the training, validation, and test sets in the ratio of 6:2:2. First, a localization model using Faster R-CNN was trained to identify and locate the target thoracolumbar junction, then a 3D AnatomyNet model was trained to finely segment the target vertebrae in the localized image. Finally, 3D DenseNet model was applied for detecting OVCF on target vertebrae. Manual annotation by experienced radiologists and a clinically made diagnosis of OVCF were used as the gold standards. The performance of the detecting system was evaluated through the area under the curve (AUC) for receiver operating characteristic (ROC) analysis, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV). Results Our automated segmentation method achieved a mean Dice coefficient of 0.95 for vertebral bodies (T12-L2) segmentation on the testing dataset, with dice coefficients greater than 0.9 accounting for 96.6%. For the diagnostic performance of our system for OVCF, the AUC, sensitivity, specificity, PPV and NPV for the four-fold cross-validation on the testing dataset were 98.1%, 95.7%, 92.6%, 91.7% and 96.2%, respectively. Conclusions A deep-learning system has been developed to automatically segment vertebral bodies and accurately detect OVCF using low-dose CT.