Cardiac Left Ventricular Ultrasound Image Sequence Recognition and Tracking A novel approach combining faster regionbased convolutional neural network (Faster RCNN) with active shape model is proposed to automatically recognize and track cardiac ultrasound left ventricular image sequences in this work. Because the shape and appearance of the left ventricle vary considerably between adjacent images, conventional methods cannot effectively identify its position and then accurately track them. We propose a novel method, incorporating Faster RCNN and active shape model, to automatically recognize, segment and track the cardiac ultrasound left ventricular image sequences. Compared with four stateoftheart approaches, the proposed method has most of better performance in SorensenDice coefficient, mean absolute deviation, and Hausdorff distance.

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%).