  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.
