  Accurate geometric quantification of the human heart is a key step in the
diagnosis of numerous cardiac diseases, and in the management of cardiac
patients. Ultrasound imaging is the primary modality for cardiac imaging,
however acquisition requires high operator skill, and its interpretation and
analysis is difficult due to artifacts. Reconstructing cardiac anatomy in 3D
can enable discovery of new biomarkers and make imaging less dependent on
operator expertise, however most ultrasound systems only have 2D imaging
capabilities. We propose both a simple alteration to the Pix2Vox++ networks for
a sizeable reduction in memory usage and computational complexity, and a
pipeline to perform reconstruction of 3D anatomy from 2D standard cardiac
views, effectively enabling 3D anatomical reconstruction from limited 2D data.
We evaluate our pipeline using synthetically generated data achieving accurate
3D whole-heart reconstructions (peak intersection over union score > 0.88) from
just two standard anatomical 2D views of the heart. We also show preliminary
results using real echo images.

Image and Video Processing;Artificial Intelligence;Computer Vision and Pattern Recognition