A U-snake based deep learning network for right ventricle segmentation.

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Abstract

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.This paper proposed a method named U-Snake network which is based on the improvement of deep snake5 together with level set8 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 different 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 test1 and test2 datasets in the Right Ventricular Segmentation Challenge, which shows the effectiveness.The experiment showed that we have obtained good result in the right ventricle segmentation challenge(RVSC). The highest segmentation accuracy on the right ventricular test set 2 reached a dice coefficient of 0.911, and the segmentation speed reached 5fps.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-net27 , Inception cnn33 , Dilated cnn29 , etc. However, it can only be used as an auxiliary tool instead of replacing the work of human beings. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.