# 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:**

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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.

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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.

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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

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

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

MSC Code:

92B05

Keywords: