1. Segmentation of cardiac MRI using particle filters
Leyla Imanirad, University of Toronto
Edward S. Rogers Sr. Department of Electrical and Computer Engineering
Institute of Biomaterials & Biomedical Engineering (IBBME)

2. Outline Objective & motivation of project Problem description
Methodology
Particle filters
Results
Future directions

3. Objective & Motivation
Automatic segmentation of left ventricle (LV) in 4D(3D + time) MRI
Segmentation data is used in derivation of physiological parameters
Manual segmentation of D-images is time-consuming and error-prone
Figure 1. Cardiac segmentation

4. Problem Description Initialize (r , d) on the first line
Estimate these values for other radial lines in the same frame using particle filters
r d
Figure 2. Sample short-axis cardiac image

5. Methodology
Use gradient values along the line and weighted sample set from previous line to construct new sample set
Figure 3. Sample initialization on line 1

6. Particle Filters Select particles with larger weights
Apply motion model to particles
Update weights based on a likelihood function
Figure 4. Example of different samples

7. Intermediate Results
Figure 5. Intermediate results for frame 8

8. Final Results

9. Future Work
Improving results by spatial cross-coupling between estimated states for each line
Propagating results to successive frames
Using more accurate motion model
Testing with different set of parameters