  We propose to formulate point cloud extraction from ultrasound volumes as an
image segmentation problem. Through this convenient formulation, a quick
prototype exploring various variants of the Residual Network, U-Net, and the
Squeeze and Excitation Network was developed and evaluated. This report
documents the experimental results compiled using a training dataset of five
labeled ultrasound volumes and 84 unlabeled volumes that got completed in a
two-week period as part of a submission to the open challenge "3D Surface Mesh
Estimation for CVPR workshop on Deep Learning in Ultrasound Image Analysis".
Based on external evaluation performed by the challenge's organizers, the
framework came first place on the challenge's
\href{https://www.cvpr2023-dl-ultrasound.com/}{Leaderboard}. Source code is
shared with the research community at a
\href{https://github.com/lisatwyw/smrvis}{public repository}.
