A Novel Fully Automated CAD System for Left Ventricle Volume Estimation
Source of Publication
2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018
© 2018 IEEE. Left ventricular (LV) volumes, and emptying and filling function remain important indices in conditions such as heart failure. These parameters are derived from the volume curve contained by the inner border of the LV of the heart, throughout the emptying and filling phases of the cardiac cycle, and the peak emptying and filling rates. The gold standard uses the Simpson rule to estimate volume from stacks of short axis images acquired using cine MRI. In this study, a deep learning, automated supervised approach to estimate ventricular volumes is introduced. Unlike prior methods that required hand-crafted image features to segment the inner contour, the proposed approach uses an automatically selected region of interest (ROI), and intelligently determines the optimum features directly from the ROI information. These derived features are then inputted into a deep learning regression model, with the estimated volume as the output results.
Institute of Electrical and Electronics Engineers Inc.
Autoencoder, CNN, Deep learning, Left ventricle, neural network
Dekhil, Omar; Taher, Fatma; Khalifa, Fahmi; Beache, Garth; Elmaghraby, Adel; and El-Baz, Ayman, "A Novel Fully Automated CAD System for Left Ventricle Volume Estimation" (2019). All Works. 203.
Indexed in Scopus