Key result
The proposed automatic segmentation method using local binary fitting and dynamic programming achieved 93.5% good contours and an overlapping dice metric of 0.91 compared to expert manual segmentation.
Why the study?
Does an automatic segmentation method based on LBF and dynamic programming accurately segment the left ventricle in cardiac MRI compared to manual expert segmentation?
Does an automatic segmentation method based on LBF and dynamic programming accurately segment the left ventricle in cardiac MRI compared to manual expert segmentation?
The proposed automatic segmentation method using LBF and dynamic programming accurately segments the left ventricle in cardiac MRI, showing high agreement with expert manual segmentation for clinical parameters like LV mass and ejection fraction.
May streamline LV analysis in cardiac MRI; leaves open multicenter validation before clinical adoption.
Segmentation of the left ventricle is very important to quantitatively analyze global and regional cardiac function from magnetic resonance. The aim of this study is to develop a novel algorithm for segmenting left ventricle on short-axis cardiac magnetic resonance images (MRI) to improve the performance of computer-aided diagnosis (CAD) systems. In this research, an automatic segmentation method for left ventricle is proposed on the basis of local binary fitting (LBF) model and dynamic programming techniques. The validation experiments are performed on a pool of data sets of 45 cases. For both endo- and epi-cardial contours of our results, percentage of good contours is about 93.5%, the average perpendicular distance are about 2 mm. The overlapping dice metric is about 0.91. The regression and determination coefficient between the experts and our proposed method on the LV mass is 1.038 and 0.9033, respectively; they are 1.076 and 0.9386 for ejection fraction (EF). The proposed segmentation method shows the better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in cardiovascular diseases.
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Hu et al. (2014) studied Heart failure, hypertrophy, and normal cardiac function (n=45). Automatic segmentation method using local binary fitting model and dynamic programming vs. Manual segmentation by clinical experts was evaluated on Percentage of good contours (average distance from ground truth < 5 mm). The proposed automatic segmentation method using local binary fitting and dynamic programming achieved 93.5% good contours and an overlapping dice metric of 0.91 compared to expert manual segmentation.
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