A fully convolutional network feature descriptor: Application to left ventricle motion estimation based on graph matching in short-axis MRI

摘要

Cardiac diseases cause abnormal motion dynamics over the cardiac cycle, and can, therefore, be diagnosed by analyzing the myocardial motion of the left ventricle (LV). In this paper, a feature point descriptor using fully convolutional neural networks (FCNs) is proposed and applied to cardiac motion estimation based on graph matching. A fully convolutional network is trained to predict endocardial contours and extract features of points from short-axis cine magnetic resonance (MR) images. An LV graph is constructed using the extracted point features, and a convex graph matching cost function is defined to estimate the point correspondence between images in two given phases. The sparsity and double stochastic constraints are introduced into the cost function, which is optimized iteratively by the alternating direction method of multipliers (ADMM). Finally, the transformation using compact supported radial basis functions with sparsity constraint is employed to estimate the dense displacement field between two cardiac images in two phases based on the correspondence relationship. The performance of the proposed method was evaluated on two public cardiac databases, and the experimental results show that the FCN feature descriptor outperforms traditional feature descriptors in estimating the correspondence between endocardial contours of the LV. For LV motion estimation, the proposed method provides more accurate motion fields than existing graph matching algorithms. (c) 2019 Elsevier B.V. All rights reserved.