UCLA

Studies in clinical medicine often demand the quantitative analysis of medical images. These tasks need careful and time-consuming tracing and labeling on fine image structures, cost expensive medical expert labor and often suffer from low reproducibility. We present a collection of methods that quantify important parameters from cardiac computed tomography (CT) and magnetic resonance imaging (MRI) in a fully-automated mode. We first present atlas-based segmentation, active contours models and graph-based segmentation which are components in our novel framework. We then present our algorithms for automated quantification of epicardial fat volume (EFV) from non-contrast cardiac CT, automated pericardial fat volume (PFV) quantification from water\/fat-resolved whole-heart non-contrast coronary magnetic resonance angiography (MRA) and automated coronary calcium scoring (CCS) from non-contrast cardiac CT images. Algorithm quantification results are validated on test scans with ground truth annotated by expert radiologists. Our approaches may potentially be applied in a clinical setting, allowing for accurate quantification of EFV, PFV and CCS without tedious manual tracing.