A Network of Simultaneous Segmentation and Registration for Right Ventricle MRI

Abstract

Short-axis MRI segmentation of the right ventricle plays an important role in assessing the structure and function of the right ventricle. However, RV segmentation is a challenge due to its complex crescent shape. In this paper, we propose a deep learning-based method for segmenting RV using the registration of the right ventricular shape model. The RV shape probability model is constructed using training samples. Next, aU-Net is trained using the shape prior probability by employing the registration technique. The shape model is registered to the network’s predictive results to estimate a shape probability map, and a loss is defined as the Kullback-Leibler divergence between the prediction result and the shape probability map and the Kullback-Leibler divergence between the predictive result and the Ground-truth. The experimental results obtained from the cardiac automatic diagnosis challenge-medical imaging calculation and computer-aided intervention (ACDC-MICCAI) 2017 dataset show that the average 3D dice coefficient is 0.919, and the average 3D Hausdorff distance is 10.71mm. Our network has also been verified in the MICCAI2012 right ventricle segmentation challenge(RVSC) dataset. The average dice coefficient is 0.865, and the Hausdorff distance is 6. 10mm. The evaluation results show that our network outperforms the state-of-art methods in several evaluation indicators.