Key result
Fully automated dynamic search algorithm outperforms previous methods for LV border recognition.
Why the study?
Accurate and fully automated detection of left ventricular boundaries is needed to improve border definition without operator interaction.
A fully automated dynamic search algorithm improves left ventricular border recognition compared to manual tracing and previous operator-dependent algorithms.
May streamline LV border detection in workflows; leaves open prospective validation for clinical outcomes.
Initial results obtained with a simple, fully automated algorithm for detection of left ventricular boundaries are presented. The strength of this approach is the use of dynamic programming search techniques, which allow determination of local border points to be influenced by the entire global border location. The relative contributions of mask mode subtraction and the dynamic search technique are evaluated with respect to accurate border definition. These computer-determined ventricular borders are compared with hand-traced borders on subtracted and unsubtracted images. The modular dynamic search algorithm is shown to perform better than previously described algorithms, which generally require operator interaction. It is also shown that for both manual and automated techniques, ventricular borders derived from subtracted images may be significantly different from borders derived from nonsubtracted images.
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Pope et al. (1985) studied Left ventricular border recognition. Dynamic search algorithm vs. Hand-traced borders and previously described algorithms was evaluated on Accurate border definition. A fully automated dynamic search algorithm performed better than previously described algorithms for left ventricular border recognition.
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