Affiliation:
1. School of Control Science and Engineering, Shandong University , Jinan 250061, China
2. Department of General Surgery, Qilu Hospital of Shandong University , Jinan 250012, China
Abstract
Abstract
Surgical tool detection is a key technology in computer-assisted surgery, and can help surgeons to obtain more comprehensive visual information. Currently, a data shortage problem still exists in surgical tool detection. In addition, some surgical tool detection methods may not strike a good balance between detection accuracy and speed. Given the above problems, in this study a new Cholec80-tool6 dataset was manually annotated, which provided a better validation platform for surgical tool detection methods. We propose an enhanced feature-fusion network (EFFNet) for real-time surgical tool detection. FENet20 is the backbone of the network and performs feature extraction more effectively. EFFNet is the feature-fusion part and performs two rounds of feature fusion to enhance the utilization of low-level and high-level feature information. The latter part of the network contains the weight fusion and predictor responsible for the output of the prediction results. The performance of the proposed method was tested using the ATLAS Dione and Cholec80-tool6 datasets, yielding mean average precision values of 97.0% and 95.0% with 21.6 frames per second, respectively. Its speed met the real-time standard and its accuracy outperformed that of other detection methods.
Funder
China's National Key Research and Development Program
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computer Graphics and Computer-Aided Design,Human-Computer Interaction,Engineering (miscellaneous),Modeling and Simulation,Computational Mechanics
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