Team 1: Geometric and appearance modeling of vascular structures in CT and MR

Wednesday, August 3, 2011 - 9:40am - 10:00am
Keller 3-180
Stefan Atev (ViTAL Images, Inc.)
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


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:

  1. 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.

  2. 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.


    1. C. Kirbas and F. Quek. “A review of vessel extraction
      techniques and algorithms”. ACM Computing Surveys, vol. 36, pp.
      81–121, 2000.

    2. 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
    MSC Code: