Abstract
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.
Highlights
INTRODUCTIONAs an important part of clinical medicine, plays a key role in solving human diseases
Surgery, as an important part of clinical medicine, plays a key role in solving human diseases
GY.Wang et al.: Surgical Tools Detection Based on Training Sample Adaptation in Laparoscopic Videos this theoretical discovery, in this paper, we propose a novel one-stage object detection framework based on a sample adaptive process controlled by reinforcement learning, that is used to detect surgical tools quickly and accurately
Summary
As an important part of clinical medicine, plays a key role in solving human diseases. (3) The one-stage object detection supported by our proposed framework based on reinforcement learning control sample adaptation achieves better performance than other object detection methods on the cholecystectomy surgery datasets m2cai16-tool-locations and AJUSet [19]. The idea of the optimization module is to use the reinforcement learning framework [18], [35] to deform the negative sample candidate box to reach the standard of positive samples. B. OPTIMIZATION MODULE The optimization module operates the candidate box in the negative sample N , and utilizes the agent under the reinforcement learning framework to perform a series of deformation operations to reach the the positive sample standard.
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