3D Left Ventricular Segmentation from 2D Cardiac MR Images Using Spatial Context Accurate left ventricular (LV) segmentation in cardiac MRI facilitates quantification of clinical parameters such as LV volume and ejection fraction (EF). We present a CNNbased method to obtain a 3D representation of LV by integrating information from 2D shortaxis and horizontal and vertical longaxis images. Our CNN is flexible to the number of input slices and uses an additional input of image coordinates as spatial context. This concept is validated on variations of two wellknown CNN architectures for medical image segmentation: UNet and DeepMedic. Fivefold cross validation on a dataset of 20 patients achieved a correlation of 95.093.1(%) for quantification of enddiastolic volume, 91.690.8(%) for endsystolic volume and 80.584.5(%) for EF for the two architectures respectively. We show that (1) incorporating longaxis data improves segmentation performance and (2) providing spatial context by adding image coordinates as input to the CNN yields similar performance with a smaller receptive field.

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%).