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}.

翻译：本文提出将超声体积中的点云提取问题重构为图像分割任务。基于这一简便框架，我们快速开发并评估了包含残差网络、U-Net及挤压激励网络多种变体的原型系统。本报告记录了使用五份标注超声体积与八十四份未标注超声体积构成的训练数据集在两周内完成的实验成果，该工作作为投稿提交至"CVPR 2023超声图像分析深度学习研讨会三维表面网格估计"开放挑战赛。根据挑战赛组织方开展的外部评估，本框架在挑战赛排行榜上位列首位。研究源代码已通过公共仓库向学术界开放共享。