On-the-Fly Point Annotation for Fast Medical Video Labeling

TL;DR

This paper introduces a live, on-the-fly point annotation method for medical videos that significantly speeds up the labeling process by maintaining a continuous cursor on objects, reducing annotation time and improving detection accuracy.

Contribution

The paper presents a novel live video annotation approach that uses a single-point cursor to streamline medical image labeling, outperforming traditional methods in speed and accuracy.

Findings

Annotation speed was 3.2 times faster than traditional methods.

Achieved a mean improvement of 6.51 ± 0.98 AP@50 over conventional annotation.

Method is easy to implement and accelerates deep learning integration in medical video analysis.

Abstract

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.