3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata
This work addresses the need for efficient cardiac shape prediction in epidemiological studies, though it appears incremental as it combines existing techniques in a novel way for this specific domain.
The authors tackled the problem of fully automatic large-scale 3D cardiac shape analysis by proposing a deep neural network that uses both CMR images and patient metadata to predict shape parameters, achieving significant agreement with reference shapes in terms of volume, mass, and distance metrics on a dataset of 500 3D shapes.
Large prospective epidemiological studies acquire cardiovascular magnetic resonance (CMR) images for pre-symptomatic populations and follow these over time. To support this approach, fully automatic large-scale 3D analysis is essential. In this work, we propose a novel deep neural network using both CMR images and patient metadata to directly predict cardiac shape parameters. The proposed method uses the promising ability of statistical shape models to simplify shape complexity and variability together with the advantages of convolutional neural networks for the extraction of solid visual features. To the best of our knowledge, this is the first work that uses such an approach for 3D cardiac shape prediction. We validated our proposed CMR analytics method against a reference cohort containing 500 3D shapes of the cardiac ventricles. Our results show broadly significant agreement with the reference shapes in terms of the estimated volume of the cardiac ventricles, myocardial mass, 3D Dice, and mean and Hausdorff distance.