Soft Plaque Detection and Automatic Vessel Segmentation

The ability to detect and measure non-calcified plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This is a particularly challenging problem in computed tomography angiography (CTA) imagery because plaques may have similar appearance to nearby blood and muscle tissue. This paper presents an effective technique for automatically detecting soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. The proposed method identifies plaques that exist within the vessel wall by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies that allow the complex appearances of plaques and vessels to be modeled with simple statistics. This method is shown to be an effective way to detect the minute variations that distinguish plaques from healthy tissue. Experiments demonstrating the effectiveness of the algorithm are performed on eight datasets, and results are compared with detections provided by an expert cardiologist.