Segmentation of Right Ventricle in Cardiac MR Images Using Shape Regression

Accurate and automatic segmentation of the right ventricle is challenging due to its complex anatomy and large shape variation observed between patients. In this paper the ability of shape regression is explored to segment right ventricle in presence of large shape variation among the patients. We propose a robust and efficient cascaded shape regression method which iteratively learns the final shape from a given initial shape. We use gradient boosted regression trees to learn each regressor in the cascade to take the advantage of supervised feature selection mechanism. A novel data augmentation method is proposed to generate synthetic training samples to improve regressors performance. In addition to that, a robust fusion method is proposed to reduce the the variance in the predictions given by different initial shapes, which is a major drawback of cascade regression based methods. The proposed method is evaluated on an image set of 45 patients and shows high segmentation accuracy with dice metric of

$$0.87\pm 0.06$$

. Comparative study shows that our proposed method performs better than state-of-the-art multi-atlas label fusion based segmentation methods.