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.

Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs Cardiac left ventricle (LV) quantification provides a tool for diagnosing cardiac diseases. Automatic calculation of all relevant LV indices from cardiac MR images is an intricate task due to large variations among patients and deformation during the cardiac cycle. Typical methods are based on segmentation of the myocardium or direct regression from MR images. To consider cardiac motion and deformation, recurrent neural networks and spatiotemporal convolutional neural networks (CNNs) have been proposed. We study an approach combining stateoftheart models and emphasizing transfer learning to account for the small dataset provided for the LVQuan19 challenge. We compare 2D spatial and 3D spatiotemporal CNNs for LV indices regression and cardiac phase classification. To incorporate segmentation information, we propose an architectureindependent segmentationbased regularization. To improve the robustness further, we employ a search scheme that identifies the optimal ensemble from a set of architecture variants. Evaluating on the LVQuan19 Challenge training dataset with 5fold crossvalidation, we achieve mean absolute errors of (111 pm 76,mathrmmm2), (1.84 pm 0.90,mathrmmm) and (1.22,pm ,0.60,mathrmmm) for area, dimension and regional wall thickness regression, respectively. The error rate for cardiac phase classification is (6.7,mathrm%).