Figure 1. Segmentation of the internal carotid artery (left).
Vessel tree with the common, internal and external carotid
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
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
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
- Relative rate of vessel diameter change as a function of
- 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.
- C. Kirbas and F. Quek. “A review of vessel extraction
techniques and algorithms”. ACM Computing Surveys, vol. 36, pp.
- T. McInerney and D. Terzopoulos. “Deformable models in
medical image analysis: A survey”. Medical Image Analysis, vol.
1, pp. 91 – 108, 1996.
Optimization, Statistics and Estimation, Differential Equations
and Geometry. MATLAB programming.
Vessel segmentation, shape statistics, appearance models