Cardiac Motion Tracking in Shortaxis MRI using Siamese Convolution Network Analysis of mechanical properties of left ventricle (LV) myocardium for shortaxis MRI is essential to diagnosis and treatment of cardiovascular diseases. However, the LV myocardium appears highly homogeneous, and there are few features inside the myocardium in cine MRI, which makes it difficult to track motion of the myocardium. In this paper, we propose a novel approach for motion tracking of the myocardium in MR image sequences. We constructed a Siamese convolution network with dense connections for feature extraction of points located on the myocardium contour. Based on a  L_2  cost function, the matched patch pairs (positive pairs) and unmatched patch pairs (negative pairs) are used to train our network. Next, given a patch centered at the point located on the myocardium contour, the trained network is employed to track corresponding patches in the image at other time points. The robust point matching is performed between the given contour and the tracking contour of the myocardium to ensure the tracking contours smooth. Our proposed approach is evaluated on the public dataset, and evaluation results show that our approach achieves more accurate tracking accuracy compared with the stateoftheart methods.

Left Ventricle Motion Estimation Based on Unsupervised Recurrent Neural Network Left ventricle (LV) motion estimation plays an essential role in understanding the pathology of heart diseases and diagnosing and treatment of heart diseases. In this paper, we propose a recurrent registration network ( R2  Net) with convolutional neural networks (CNNs) to estimate the motion of left ventricle using cardiac MR image sequences. Our  R2  Net network is composed of a CNN, a function compositor, and a spatial transformer. The CNN is an asymmetric Unet architecture, which evaluates the correspondence between two images and outputs the sparse deformation field between two input images. Consecutive images are input to the CNN, and consecutive deformation fields are estimated. The compositor composites two deformation fields at adjacent time points together to generate a composited deformation field. The transformer warps the cardiac MR image at ED using the composited deformation field, and generates a deformed image. By composting successive deformation fields onebyone, the final deformation field between ED and ES is estimated. We define a loss function without ground truth and train the  R2  Net in an unsupervised way. Moreover, the bending energy is introduced to the loss function to impose the deformation field smooth and topologypreserving. Our method is evaluated using public datasets. Experimental results show that our method outperforms the stateoftheart researches in respect of several evaluation metrics.