addison elliott , akhila karlapalem amy givan maria ... · addison elliott1, akhila karlapalem1,...
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RESEARCH POSTER PRESENTATION DESIGN © 2015
www.PosterPresentations.com
y = 1.308x - 158.33R² = 0.7585
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y = 1.3156x - 154.96R² = 0.7094
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Figure 4 – (3-4) Edge converted polar image with optimal path
shown in blue. (5) Cartesian image with optimal path in blue
• Dice Similarity Coefficient
• End-Systolic: 0.750±0.074
• End-Diastolic: 0.802±0.079
• Hausdorff Distance
• End-Systolic: 9.03±2.71mm
• End-Diastolic: 9.09±2.37mm
• 10 female subjects, pre- and post-intervention, 18-40 years old
• Select end-systolic (ES) and diastolic (ED) frames from scans
• 2 apex, 7 papillary muscles, 6 mitral valve (randomly chosen)
• B-mode images created from custom software from IQ files
• Wavelet’s transform used for speckle reduction
Algorithm
1. Smooth image using anisotropic diffusion
2. Convert to polar domain (origin = endocardial center)
3. Robert’s edge detection filter applied to polar image
4. DP Dijkstra’s Algorithm
1. Cost function = inverted edge image
2. Moves from left->right angular dimension (loops back
around), stops at start point
5. Convert path back to Cartesian domain
METHODS
RESULTSDISCUSSION AND CONCLUSIONS
• Simple validation of algorithm to segment endocardium
• Limitations
• Algorithm purely based on image content, messes up for
missing or weak boundaries
• Only one observer, not providing any context of inter-
observer variability
• Future Work
• Factor in temporal dimension (easier to see with motion)
• Use shape model or ASMs to segment boundaries
• Integrate algorithm in one software package to eventually
fit a shape model to
• Coronary heart disease is the #1 leading cause of death
globally
• Echocardiography is the first step in assessing cardiac health
• Ultrasound is inexpensive, portable and non-invasive
compared to MRI
• Measures such as LVEF, LV mass/volume difficult to measure
manually
• Automatic segmentation allow easy calculation of measures
• Objective: Create automatic segmentation algorithm to
segment the endocardium in PSAX views. Validate to 1
expert tracer
Addison Elliott1, Akhila Karlapalem1, Amy Givan2, Maria Fernandez-del-Valle2, Jon Klingensmith1
1 Department of Electrical and Computer Engineering, Southern Illinois University Edwardsville, Edwardsville, Illinois, 2 Department of Applied Health, Southern Illinois University Edwardsville, Edwardsville, Illinois
Endocardium Segmentation in Parasternal Short-axis Echocardiograms using Dynamic Programming
Figure 1 – Diagram of parasternal short-axis views
Figure 5 – Left: Subject 8 ED mitral valve PSAX. Right: Plot of
computer & observer CSA with black line Hausdorff Dist.
INTRODUCTION
Figure 2 – Apical PSAX view
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Figure 6 – Bland-Altman charts (bottom) and linear regression
(top) shown for ES (left) and ED (right)
ACKNOWLEDGEMENTS
Funding provided by AHA Grant #17AIREA33670361
(Volumetric assessment of epicardial adipose tissue using
echocardiography)
IRB ID: #17-0817-1C
RESULTSMETHODS
Figure 3 – (1) Smoothed PSAX apical view. (2) Polar image from
smoothed image in (1). Endocardial center shown in (1) as red
point.