The ProID automated segmentation algorithm achieved an average Dice similarity score of 0.93 compared to manual segmentation and increased inter-operator agreement from 0.93 to 0.95.
Does the ProID algorithm improve the accuracy and inter-operator reliability of left ventricle segmentation in echocardiograms compared to manual segmentation?
The ProID algorithm provides accurate left ventricle segmentation across echo platforms and reduces inter-operator variability without the need for training data.
Absolute Event Rate: 0.93% vs 0.93%
We present a user-initialized, automated left ventricle (LV) segmentation method for use with echocardiograms (echo). The method uses an iterative Dijkstra's algorithm, strategic node selection, and novel cost matrix formulation based on intensity peak prominence and is termed the "Prominence Iterative Dijkstra's" algorithm, or ProID. ProID is initialized with three user-input clicks per time-series scan. ProID was tested using artificial echos representing five different systems. Results showed accurate LV contours and volume estimations as compared to the ground-truth for all systems. Using the CAMUS dataset, we demonstrate ProID maintained similar Dice similarity scores (DSS) to other automated methods. ProID was then used to analyze a clinical cohort of 66 pediatric patients, including normal and diseased hearts. Output segmentations, LV volume, and ejection fraction were compared against manual segmentations from two expert readers. ProID maintained an average DSS of 0.93 when comparing against manual segmentation. Comparing the two expert readers, the manual segmentations maintained a DSS of 0.93 which increased to 0.95 when they used ProID. Thus, ProID reduced inter-operator variability across the expert readers. Overall, this work demonstrates ProID yields accurate boundaries across age groups, disease states, and echo platforms with low computational cost and no need for training data.
Brindise et al. (Fri,) conducted a other in Left ventricle segmentation in echocardiograms (n=66). ProID (Prominence Iterative Dijkstra's algorithm) vs. Manual segmentation by expert readers was evaluated on Dice similarity score (DSS) compared to manual segmentation. The ProID automated segmentation algorithm achieved an average Dice similarity score of 0.93 compared to manual segmentation and increased inter-operator agreement from 0.93 to 0.95.