Segmentation of myocardium from cardiac MR images using a novel dynamic programming based segmentation method.

PURPOSE

Myocardium segmentation in cardiac magnetic resonance (MR) images plays a vital role in clinical diagnosis of the cardiovascular diseases. Because of the low contrast and large variation in intensity and shapes, myocardium segmentation has been a challenging task. A dynamic programming (DP) based segmentation method, incorporating the likelihood and shape information of the myocardium, is developed for segmenting myocardium in cardiac MR images.

METHODS

The endocardium, i.e., the left ventricle blood cavity, is segmented for initialization, and then the optimal epicardium contour is determined using the polar-transformed image and DP scheme. In the DP segmentation scheme, three techniques are proposed to improve the segmentation performance: (1) the likelihood image of the myocardium is constructed to define the external cost in the DP, thus the cost function incorporates prior probability estimation; (2) the adaptive search range is introduced to determine the polar-transformed image, thereby excluding irrelevant tissues; (3) the connectivity constrained DP algorithm is developed to obtain an optimal closed contour. Four metrics, including the Dice metric (Dice), root mean squared error (RMSE), reliability, and correlation coefficient, are used to assess the segmentation accuracy. The authors evaluated the performance of the proposed method on a private dataset and the MICCAI 2009 challenge dataset. The authors also explored the effects of the three new techniques of the DP scheme in the proposed method.

RESULTS

For the qualitative evaluation, the segmentation results of the proposed method were clinically acceptable. For the quantitative evaluation, the mean (Dice) for the endocardium and epicardium was 0.892 and 0.927, respectively; the mean RMSE was 2.30 mm for the endocardium and 2.39 mm for the epicardium. In addition, the three new techniques in the proposed DP scheme, i.e., the likelihood image of the myocardium, the adaptive search range, and the connectivity constrained DP algorithm, improved the segmentation performance for the epicardium with 0.029, 0.047, and 0.007 in terms of the Dice and 0.98, 1.31, and 0.21 mm in terms of the RMSE, respectively.

CONCLUSIONS

The three techniques (the likelihood image of the myocardium, the adaptive search range, and the connectivity constrained DP algorithm) can improve the segmentation ability of the DP method, and the proposed method with these techniques has the ability to achieve the acceptable segmentation result of the myocardium in cardiac MR images. Therefore, the proposed method would be useful in clinical diagnosis of the cardiovascular diseases.