Purpose: In medical research, deep learning models rely on high-quality annotated data, a process often laborious and timeconsuming. This is particularly true for detection tasks where bounding box annotations are required. The need to adjust two corners makes the process inherently frame-by-frame. Given the scarcity of experts' time, efficient annotation methods suitable for clinicians are needed. Methods: We propose an on-the-fly method for live video annotation to enhance the annotation efficiency. In this approach, a continuous single-point annotation is maintained by keeping the cursor on the object in a live video, mitigating the need for tedious pausing and repetitive navigation inherent in traditional annotation methods. This novel annotation paradigm inherits the point annotation's ability to generate pseudo-labels using a point-to-box teacher model. We empirically evaluate this approach by developing a dataset and comparing on-the-fly annotation time against traditional annotation method. Results: Using our method, annotation speed was 3.2x faster than the traditional annotation technique. We achieved a mean improvement of 6.51 +- 0.98 AP@50 over conventional method at equivalent annotation budgets on the developed dataset. Conclusion: Without bells and whistles, our approach offers a significant speed-up in annotation tasks. It can be easily implemented on any annotation platform to accelerate the integration of deep learning in video-based medical research.

翻译：目的：在医学研究中，深度学习模型依赖高质量标注数据，而这一过程通常费时费力。对于需要边界框标注的检测任务尤其如此。调整两个角点的需求使得标注过程本质上是逐帧进行的。鉴于专家时间有限，亟需适用于临床医生的高效标注方法。方法：我们提出一种面向实时视频标注的即时标注方法，以提高标注效率。该方法通过将光标持续对准实时视频中的目标物体来维持连续单点标注，避免了传统标注方法中繁琐的暂停和重复导航操作。这种新型标注范式继承了点标注通过点转框教师模型生成伪标签的能力。我们通过构建数据集，并将即时标注时间与传统方法进行比较，对该方法进行了实证评估。结果：使用我们的方法，标注速度比传统标注技术快3.2倍。在同等标注预算下，我们在所构建数据集上的平均AP@50提升了6.51±0.98，优于传统方法。结论：我们的方法无需复杂附加操作即可显著加速标注任务。该方法可轻松部署于任何标注平台，以加速基于视频的医学研究中深度学习的整合。