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Purpose: Ventricular segmentation is of great importance for the heart condition monitoring. However, manual segmentation is time‐consuming, cumbersome, and subjective. Many segmentation methods perform poorly due to the complex structure and uncertain shape of the right ventricle, so we combine deep learning to achieve automatic segmentation. Method: This paper proposed a method named U‐Snake network which is based on the improvement of deep snake together with level set to segment the right ventricular in the MR images. U‐snake aggregates the information of each receptive field which is learned by circular convolution of multiple dilation rates. At the same time, we also added dice loss functions and transferred the result of U‐Snake to the level set so as to further enhance the effect of small object segmentation. Our method is tested on the test 1 and test 2 datasets in the right ventricular segmentation challenge (RVSC), which shows the effectiveness. Results: The experiment showed that we have obtained good result in the RVSC. The highest segmentation accuracy on the right ventricular test set 2 reached a dice coefficient of 0.911, and the segmentation speed reached 5 fps. Conclusions: Our method, a new deep learning network named U‐snake, has surpassed the previous excellent ventricular segmentation method based on mathematical theory and other classical deep learning methods, such as Residual U‐net, Inception CNN, and Dilated CNN. However, it can only be used as an auxiliary tool instead of replacing the work of human beings. [ABSTRACT FROM AUTHOR]