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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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Endo- and epi-cardial contours from 3 cases derived from two methods.For each case, two images are shown. The left one is the segmentation result from our previous method; while the right one is derived from our present algorithm. The ground truth from experts is drawn in solid red curve; whereas the dashed blue one represents the results of our previous or present method.
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pone-0114760-g007: Endo- and epi-cardial contours from 3 cases derived from two methods.For each case, two images are shown. The left one is the segmentation result from our previous method; while the right one is derived from our present algorithm. The ground truth from experts is drawn in solid red curve; whereas the dashed blue one represents the results of our previous or present method.

Mentions: Furthermore, we display segmentation outputs for both endo- and epi-cardial contours from 6 studies obtained by two approaches in Fig. 7. For each case, two resulting images are shown. The left one is the outcome from our previous method and the right one comes from our present algorithm. The dashed blue ones represent the results of our previous method or the present algorithm; while the solid red curves are the ground truth. It can be seen that the schema proposed by us does better performance than 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)

Endo- and epi-cardial contours from 3 cases derived from two methods.For each case, two images are shown. The left one is the segmentation result from our previous method; while the right one is derived from our present algorithm. The ground truth from experts is drawn in solid red curve; whereas the dashed blue one represents the results of our previous or present method.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0114760-g007: Endo- and epi-cardial contours from 3 cases derived from two methods.For each case, two images are shown. The left one is the segmentation result from our previous method; while the right one is derived from our present algorithm. The ground truth from experts is drawn in solid red curve; whereas the dashed blue one represents the results of our previous or present method.
Mentions: Furthermore, we display segmentation outputs for both endo- and epi-cardial contours from 6 studies obtained by two approaches in Fig. 7. For each case, two resulting images are shown. The left one is the outcome from our previous method and the right one comes from our present algorithm. The dashed blue ones represent the results of our previous method or the present algorithm; while the solid red curves are the ground truth. It can be seen that the schema proposed by us does better performance than 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