Right Ventricle Segmentation in Cardiac MR Images Using U-Net with Partly Dilated Convolution

Segmentation of anatomical structures in cardiac MR images is an important problem because it is necessary for evaluation of morphology of these structures for diagnostic purposes. Automatic segmentation algorithm with near-human accuracy would be extremely helpful for a medical specialist. In this paper we consider such structures as endocardium and epicardium of right ventricle. We compare the performance of the best existing neural networks such as U-Net and GridNet, and propose our own modification of U-Net which implies replacement of every second convolution layer with dilated (atrous) convolution layer. Evaluation on benchmark dataset RVSC demonstrated that the proposed algorithm allows to improve the segmentation accuracy up to 6% both for endocardium and epicardium compared to original U-Net. The algorithm also overperforms GridNet for both segmentation problems.