Efficient Frame Extraction: A Novel Approach Through Frame Similarity and Surgical Tool Tracking for Video Segmentation

TL;DR

This paper introduces KAFR, a technique that reduces video dataset size for surgical analysis by removing redundant frames through tool tracking and similarity assessment, enhancing efficiency and accuracy.

Contribution

The novel KAFR method combines tool detection and movement analysis to efficiently select useful frames in surgical videos, improving data processing for AI models.

Findings

Reduces dataset size significantly while maintaining accuracy.

Speeds up video analysis by eliminating redundant frames.

Effective on multiple surgical datasets.

Abstract

The interest in leveraging Artificial Intelligence (AI) for surgical procedures to automate analysis has witnessed a significant surge in recent years. One of the primary tools for recording surgical procedures and conducting subsequent analyses, such as performance assessment, is through videos. However, these operative videos tend to be notably lengthy compared to other fields, spanning from thirty minutes to several hours, which poses a challenge for AI models to effectively learn from them. Despite this challenge, the foreseeable increase in the volume of such videos in the near future necessitates the development and implementation of innovative techniques to tackle this issue effectively. In this article, we propose a novel technique called Kinematics Adaptive Frame Recognition (KAFR) that can efficiently eliminate redundant frames to reduce dataset size and computation time while retaining useful frames to improve accuracy. Specifically, we compute the similarity between consecutive frames by tracking the movement of surgical tools. Our approach follows these steps: $i)$ Tracking phase: a YOLOv8 model is utilized to detect tools presented in the scene, $ii)$ Similarity phase: Similarities between consecutive frames are computed by estimating variation in the spatial positions and velocities of the tools, $iii$) Classification phase: An X3D CNN is trained to classify segmentation. We evaluate the effectiveness of our approach by analyzing datasets obtained through retrospective reviews of cases at two referral centers. The newly annotated Gastrojejunostomy (GJ) dataset covers procedures performed between 2017 and 2021, while the previously annotated Pancreaticojejunostomy (PJ) dataset spans from 2011 to 2022 at the same centers.