Why the study?
Prior methods to estimate left ventricular volumes required hand-crafted image features to segment the inner contour from cine MRI.
Does a deep learning automated supervised approach accurately estimate left ventricular volumes from cine MRI compared to traditional methods?
Does a deep learning automated supervised approach accurately estimate left ventricular volumes from cine MRI compared to traditional methods?
A novel deep learning approach automates left ventricular volume estimation from cine MRI without requiring manual segmentation or hand-crafted image features.
Supports automated LV volumetry from cine MRI; leaves open prospective validation before clinical use.
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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