Quantification of cardiac bull's-eye map based on principal strain analysis for myocardial wall motion assessment in stress echocardiography

In this paper we consider automated myocardial wall motion assessment by quantifying a cardiac bull's eye map derived from principal strain analysis. The objective is to learn a classification model that can classify between normal and abnormal wall motions. A traditional hand-crafted feature a...

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Bibliographic Details
Published in2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) pp. 1195 - 1198
Main Authors Omar, Hasmila A., Domingos, Joao S., Patra, Arijit, Upton, Ross, Leeson, Paul, Noble, J Alison
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2018
Subjects
2D/3D ultrasound
cardiac bull's-eye map
CNN
Computational modeling
Feature extraction
left ventricular functionality
Machine learning
myocardial mechanics
Myocardium
principal strain analysis
Random Forest
Strain
Stress
Support vector machines
Wall motion assessment
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Summary:In this paper we consider automated myocardial wall motion assessment by quantifying a cardiac bull's eye map derived from principal strain analysis. The objective is to learn a classification model that can classify between normal and abnormal wall motions. A traditional hand-crafted feature approach based on pixel intensities is compared with a deep learning framework, where a Convolutional Neural Network (CNN) automatically learns features. Experiments on a 3D Dobutamine Stress Echo (DSE) dataset with normal and abnormal wall motions shows that both hand-crafted approaches yield comparable accuracy: Random Forests (72.1%), Support Vector Machines (70.5%), and CNN at a slightly higher accuracy (75.0%) and a lower training computational cost.
ISSN:1945-8452
DOI:10.1109/ISBI.2018.8363785