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)

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

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 80-120 2D-images is time-consuming and error-prone Figure 1. Cardiac segmentation

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

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

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

Intermediate Results Figure 5. Intermediate results for frame 8

Final Results

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