Surgical Tools Detection Based on Training Sample Adaptation in Laparoscopic Videos

The performance of object detection methods plays an important role in the recognition of surgical tools, and is a key link in the automated evaluation of surgical skills. In this paper, we propose a novel framework for one-stage object detection based on a sample adaptive process controlled by reinforcement learning, which can maintain the speed advantage while maintaining higher accuracy than two-stage object detection methods. We use m2cai16-tool-locations and AJU-Set, two datasets covering seven surgical tools with spatial information collected from hospital gallbladder surgery videos to evaluate and verify the effectiveness of our proposed framework. The experiments show that our proposed framework can make the one-stage object detection method achieve 70.1% and 77.3% accuracy on m2cai16-tool-locations and AJU-Set, respectively. We further validated the effectiveness of our proposed framework by analyzing the usage patterns, motion trajectories, and mobile values of surgical tools.