Project Description: Figure 1. Segmentation of the internal carotid artery (left). Vessel tree with the common, internal and external carotid arteries (right). Accurate vessel segmentation is required in many clinical applications, such as identifying the degree of stenosis (narrowing) of a vessel to assess if blood flow to an organ is sufficient, quantification of plaque buildup (to determine the risk of stroke, for example), and in detecting aneurisms which pose severe risks if ruptured. Proximity to bone can pose segmentation challenges due to the similar appearance of bone and contrasted vessels in CT (Figure 1 – the internal carotid has to cross the skull base); other challenges are posed by low X-ray dose images, and pathology such as stenosis and calcifications. Figure 2. Cross section of vessel segmentation from CT data, shown with straightened centerline. A typical segmentation consists of a centerline that tracks the length of the vessel, lumen surface and vessel wall surface. Since for performance reasons most clinical applications use only local vessel models for detection, tracking and segmentation, in the presence of noise the results can become physiologically unrealistic – for example in the figure above, the diameter of the lumen and wall cross-sections vary too rapidly. Figure 3. Vessel represented as a centerline with periodically sampled cross-sections in the planes orthogonal to the centerline. Note that some planes intersect, which makes this representation problematic. The in-plane cross-sections of the vessel are shown on the right. The goal of this project is to design a method for refining a vessel segmentation based on the following general approach: Choose an appropriate geometric representations for vessel segmentation (e.g., generalized cylinders) and derive the equations and methods necessary to manipulate it as required and to convert to and from the representation. One common, but sometimes problematic representation is shown in Figure 3. Learn a geometric model for vessels based on the representation from a set of training data (for example segmentations obtained from low-noise clinical images). Example model parameters: - Relative rate of vessel diameter change as a function of centerline curvature - Typical wall thickness as a function of lumen cross-section area Learn an appearance model for the vessels that captures details about how vessels appear in a clinical imaging modality such as CT. For example: - Radial lumen intensity profile in Hounsfeld units - Rate of intensity change along the centerline Compute the most likely vessel representation given a starting segmentation and the learned geometric and appearance models. The project will use real clinical data and many different types of vessels. References: C. Kirbas and F. Quek. “A review of vessel extraction techniques and algorithms”. ACM Computing Surveys, vol. 36, pp. 81–121, 2000. T. McInerney and D. Terzopoulos. “Deformable models in medical image analysis: A survey”. Medical Image Analysis, vol. 1, pp. 91 – 108, 1996. Prerequisites: Optimization, Statistics and Estimation, Differential Equations and Geometry. MATLAB programming. Keywords: Vessel segmentation, shape statistics, appearance models