Best (and Worst) Practices for Organizing a Challenge on Cardiac Biophysical Models During AI Summer: The CRTEPiggy19 Challenge During the last years tens of challenges have been organized to benchmark computational techniques with shared data. Historically, most challenges in conferences such as MICCAI have been devoted to medical image processing, especially on object recognition or segmentation tasks. Due to the increasing popularity and easy access to machine (deep) learning methods, as part of our current Artificial Intellingence (AI) summer, the number of AIrelated challenges has exploded. In parallel, the community of biophysical models also has a valuable history of organizing challenges, including synthetic and experimental data, to assess the accuracy of the resulting simulations. In this paper, the similarities and differences in computational challenges organized by these communities are reviewed, suggesting best practices and what to avoid when organizing a challenge on biophysical models. Specifically, details will be given about the preparation of the CRTEPiggy19 challenge.

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%).