Left Ventricular Parameter Regression from Deep Feature Maps of a Jointly Trained Segmentation CNN. Quantification of left ventricular (LV) parameters from cardiac MRI is important to assess cardiac condition and help in the diagnosis of certain pathologies. We present a CNNbased approach for automatic quantification of 11 LV indices: LV and myocardial area, 3 LV dimensions and 6 regional wall thicknesses (RWT). We use an encoderdecoder segmentation architecture and hypothesize that deep feature maps contain important shape information suitable to start an additional network branch for LV index regression. The CNN is simultaneously trained on regression and segmentation losses. We validated our approach on the LVQuan19 training dataset and found that our proposed CNN significantly outperforms a standard encoder regression CNN. The mean absolute error and Pearson correlation coefficient obtained for the different indices are respectively 190 mm(2) (96(%)), 214 mm(2) (0.90(%)), 2.99 mm (95(%)) and 1.82 mm (71(%)) for LV area, myocardial area, LV dimensions and RWT on a threefold cross validation and 186 mm(2) (97(%)), 222 mm(2) (0.88(%)), 3.03 mm (0.95(%)) and 1.67 mm (73(%)) on a fivefold cross validation.

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%).