In the context of medical Augmented Reality (AR) applications, object tracking is a key challenge and requires a significant amount of annotation masks. As segmentation foundation models like the Segment Anything Model (SAM) begin to emerge, zero-shot segmentation requires only minimal human participation obtaining high-quality object masks. We introduce a HoloLens-Object-Labeling (HOLa) Unity and Python application based on the SAM-Track algorithm that offers fully automatic single object annotation for HoloLens 2 while requiring minimal human participation. HOLa does not have to be adjusted to a specific image appearance and could thus alleviate AR research in any application field. We evaluate HOLa for different degrees of image complexity in open liver surgery and in medical phantom experiments. Using HOLa for image annotation can increase the labeling speed by more than 500 times while providing Dice scores between 0.875 and 0.982, which are comparable to human annotators. Our code is publicly available at: https://github.com/mschwimmbeck/HOLa

翻译：在医学增强现实（AR）应用中，物体跟踪是一个关键挑战，且需要大量标注掩码。随着 Segment Anything Model（SAM）等分割基础模型的出现，零样本分割仅需极少人工参与即可获得高质量的物体掩码。我们提出了一种基于 SAM-Track 算法的 HoloLens-Object-Labeling（HOLa）Unity 与 Python 应用程序，该应用可为 HoloLens 2 提供全自动的单物体标注，同时仅需极少人工参与。HOLa 无需针对特定图像外观进行调整，因此可促进任何应用领域的 AR 研究。我们在开放式肝脏手术和医学体模实验中，针对不同图像复杂度评估了 HOLa。使用 HOLa 进行图像标注可将标注速度提升 500 倍以上，同时提供 0.875 至 0.982 的 Dice 分数，该分数与人工标注结果相当。我们的代码公开于：https://github.com/mschwimmbeck/HOLa