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Automatic segmentation of the left ventricle in cardiac MRI using local binary fitting model and dynamic programming techniques.

Hu H, Gao Z, Liu L, Liu H, Gao J, Xu S, Li W, Huang L - PLoS ONE (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, People's Republic of China.

ABSTRACT
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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Regression curve and Bland-Altman plot for the ejection fraction (EF) and left ventricle (LV) mass: (a) linear regression for LV mass, (b) Bland-Altman plots of LV mass, (c) Linear regression for EF, (d) Bland-Altman plots of EF.
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pone-0114760-g004: Regression curve and Bland-Altman plot for the ejection fraction (EF) and left ventricle (LV) mass: (a) linear regression for LV mass, (b) Bland-Altman plots of LV mass, (c) Linear regression for EF, (d) Bland-Altman plots of EF.

Mentions: We use regression and Bland-Altman analysis to evaluate our results of 45 cases. Fig. 4 shows the regression and Bland-Altman plots for the EF and LV mass measurements. For the LV mass, the regression coefficient is very good, and the spread of the values is pretty low, the slope is 1.038, demonstrating a bias 0.27 on the Bland-Altman plot. For the EF, it can be seen that the regression coefficient is 1.076; the bias is 2.99 on the Bland-Altman plot. The coefficient of determination for LV mass and EF is 0.9033 and 0.9386. Therefore, the algorithm is pretty accurate at computing LV mass and the EF.


Automatic segmentation of the left ventricle in cardiac MRI using local binary fitting model and dynamic programming techniques.

Hu H, Gao Z, Liu L, Liu H, Gao J, Xu S, Li W, Huang L - PLoS ONE (2014)

Regression curve and Bland-Altman plot for the ejection fraction (EF) and left ventricle (LV) mass: (a) linear regression for LV mass, (b) Bland-Altman plots of LV mass, (c) Linear regression for EF, (d) Bland-Altman plots of EF.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4263664&req=5

pone-0114760-g004: Regression curve and Bland-Altman plot for the ejection fraction (EF) and left ventricle (LV) mass: (a) linear regression for LV mass, (b) Bland-Altman plots of LV mass, (c) Linear regression for EF, (d) Bland-Altman plots of EF.
Mentions: We use regression and Bland-Altman analysis to evaluate our results of 45 cases. Fig. 4 shows the regression and Bland-Altman plots for the EF and LV mass measurements. For the LV mass, the regression coefficient is very good, and the spread of the values is pretty low, the slope is 1.038, demonstrating a bias 0.27 on the Bland-Altman plot. For the EF, it can be seen that the regression coefficient is 1.076; the bias is 2.99 on the Bland-Altman plot. The coefficient of determination for LV mass and EF is 0.9033 and 0.9386. Therefore, the algorithm is pretty accurate at computing LV mass and the EF.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, People's Republic of China.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus