Automatic labelling of coronary arteries

Automatically assigning the correct anatomical labels to coronary arteries is an important task that would speed up work flow times of radiographers, radiologists and cardiologists, and also aid the standard assessment of coronary artery disease. However, automatic labelling faces challenges resulting from structures as complex and widely varied as coronary anatomy. A system has been developed which addresses this requirement and is capable of automatically assigning correct anatomical labels to pre-segmented coronary artery centrelines in Cardiac Computed-Tomography Angiographic (CCTA) images with 84% accuracy. The system consists of two major phases: 1) training a multivariate gaussian classifier with labelled anatomies to estimate mean-vectors for each anatomical class and a covariance matrix pooled over all classes, based on a set of features; 2) generating all plausible label combinations per test anatomy based on a set of topological and geometric rules, and returning the most likely based on the parameters generated in 1).