Why the study?
LV volumes and emptying and filling function are important indices in conditions such as heart failure, but prior methods required hand-crafted image features to segment the inner contour from cine MRI.
Does a deep learning automated CAD system accurately estimate left ventricular volume from cine MRI compared to standard methods?
Does a deep learning automated CAD system accurately estimate left ventricular volume from cine MRI compared to standard methods?
A novel deep learning regression model automates left ventricular volume estimation directly from automatically selected ROIs in cine MRI, eliminating the need for hand-crafted image features.
May facilitate automated LV volumetry in research cine MRI; leaves open prospective clinical validation of accuracy.
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.
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Dekhil et al. (2018) studied this question.
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