Abstract
Summary Manual segmentation of CMR images is inefficient and inconsistent method for measuring ventricular volumes. In this study, a new technique (ACOISIT) is developed and implemented for measuring ventricular volumes. The technique is based on automatic delineation of bloodmyocardium border using ant colony optimization with salient isolated thresholding. The technique was implemented on datasets from eight volunteers and the results were compared to manual segmentation. ACOISIT showed good agreement with the gold standard and was faster and more consistent than manual segmentation. Background
Highlights
The technique is based on automatic delineation of bloodmyocardium border using ant colony optimization with salient isolated thresholding
Ventricular volumes are usually measured from cardiovascular magnetic resonance (CMR) images through manual delineation of the left and right ventricles (LV, RV) endocardial contours in parallel short-axis images
The ACOISIT technique was implemented on all images to delineate LV and RV endocardial contours, from which ventricular volumes were measured using Simpson’s approximation, and compared to manual segmentation
Summary
Ventricular volumes are usually measured from cardiovascular magnetic resonance (CMR) images through manual delineation of the left and right ventricles (LV, RV) endocardial contours in parallel short-axis images. Manual segmentation consumes long processing time and reduces measurement consistency. A new artificial intelligence technique (ant colony optimization with iterative salient isolated thresholding, ACOISIT) is developed and implemented for measuring ventricular volumes through automatic delineation of blood-myocardium border
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