
Real‐time surgical instrument detection in robot‐assisted surgery using a convolutional neural network cascade
Author(s) -
Zhao Zijian,
Cai Tongbiao,
Chang Faliang,
Cheng Xiaolin
Publication year - 2019
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2019.0064
Subject(s) - surgical robot , convolutional neural network , surgical instrument , computer science , cascade , artificial intelligence , robot , surgical procedures , computer vision , medicine , surgery , engineering , chemical engineering
Surgical instrument detection in robot‐assisted surgery videos is an import vision component for these systems. Most of the current deep learning methods focus on single‐tool detection and suffer from low detection speed. To address this, the authors propose a novel frame‐by‐frame detection method using a cascading convolutional neural network (CNN) which consists of two different CNNs for real‐time multi‐tool detection. An hourglass network and a modified visual geometry group (VGG) network are applied to jointly predict the localisation. The former CNN outputs detection heatmaps representing the location of tool tip areas, and the latter performs bounding‐box regression for tool tip areas on these heatmaps stacked with input RGB image frames. The authors’ method is tested on the publicly available EndoVis Challenge dataset and the ATLAS Dione dataset. The experimental results show that their method achieves better performance than mainstream detection methods in terms of detection accuracy and speed.