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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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Endocardial contours of two slice images from case SC-HF-I-09 and SC-HF-I-40.The blue curves are obtained using our present algorithm, whereas the green ones are from our previous method. The red curves are the ground truth.
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pone-0114760-g006: Endocardial contours of two slice images from case SC-HF-I-09 and SC-HF-I-40.The blue curves are obtained using our present algorithm, whereas the green ones are from our previous method. The red curves are the ground truth.

Mentions: To demonstrate the advantages of our proposed method, we compare the derived endocardial contours from 2 studies (SC-HF-I-09, SC-HF-I-40) obtained from two methods in Fig. 6. For each case, two resulting images are shown. The blue curves come from our present method, whereas the green ones are from our previous method. The red curves are the ground truth. The comparison results indicated that the endocardial outlines segmented by our proposed method match experts' outlines more closely, compared to our previous method.


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)

Endocardial contours of two slice images from case SC-HF-I-09 and SC-HF-I-40.The blue curves are obtained using our present algorithm, whereas the green ones are from our previous method. The red curves are the ground truth.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0114760-g006: Endocardial contours of two slice images from case SC-HF-I-09 and SC-HF-I-40.The blue curves are obtained using our present algorithm, whereas the green ones are from our previous method. The red curves are the ground truth.
Mentions: To demonstrate the advantages of our proposed method, we compare the derived endocardial contours from 2 studies (SC-HF-I-09, SC-HF-I-40) obtained from two methods in Fig. 6. For each case, two resulting images are shown. The blue curves come from our present method, whereas the green ones are from our previous method. The red curves are the ground truth. The comparison results indicated that the endocardial outlines segmented by our proposed method match experts' outlines more closely, compared to our previous method.

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