Automated aortic calcium scoring on low-dose chest computed tomography

Methods: The system was trained and tested on scans from participants of a lung cancer screening trial. A total of 433 low-dose, non-ECG-synchronized, noncontrast-enhanced 16 detector row examinations of the chest was randomly divided into 340 training and 93 test data sets. A first observer manually identified aortic calcifications on training and test scans. A second observer did the same on the test scans only. First, a multiatlas-based segmentation method was developed to delineate the aorta. Segmented volume was thresholded and potential calcifications (candidate objects) were extracted by three-dimensional connected component labeling. Due to image resolution and noise, in rare cases extracted candidate objects were connected to the spine. They were separated into a part outside and parts inside the aorta, and only the latter was further analyzed. All candidate objects were represented by 63 features describing their size, position, and texture. Subsequently, a two-stage classification with a selection of features and k-nearest neighbor classifiers was performed. Based on the detected aortic calcifications, total calcium volume score was determined for each subject. Results: The computer system correctly detected, on the average, 945 mm(3) out of 965 mm(3) (97.9%) calcified plaque volume in the aorta with an average of 64 mm(3) of false positive volume per scan. Spearman rank correlation coefficient was rho=0.960 between the system and the first observer compared to rho=0.961 between the two observers. Conclusions: Automatic calcium scoring in the aorta thus appears feasible with good correlation between manual and automatic scoring.