Real-time surgical tool detection in computer-aided surgery based on enhanced feature-fusion convolutional neural network. Issue 3 (24th May 2022)

• Record Type: Journal Article
• Authors: Liu, Kaidi; Zhao, Zijian; Shi, Pan; Li, Feng; Song, He
• Is Part Of: Journal of computational design and engineering. Volume 9:Issue 3(2022)
• Page Start: 1123
• Page End: 1134
• Publication Date: 2022-05-24
• Subjects: feature fusion -- convolutional neural network -- real-time surgical tool detection -- computer-assisted surgery; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1093/jcde/qwac049
• Languages: English
• ISSNs: 2288-4300

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. Graphical Abstract: