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Real-time surgical instrument detection in robot-assisted surgery using a convolutional neural network cascade.

Authors :
Zhao Z
Cai T
Chang F
Cheng X
Source :
Healthcare technology letters [Healthc Technol Lett] 2019 Nov 26; Vol. 6 (6), pp. 275-279. Date of Electronic Publication: 2019 Nov 26 (Print Publication: 2019).
Publication Year :
2019

Abstract

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.

Details

Language :
English
ISSN :
2053-3713
Volume :
6
Issue :
6
Database :
MEDLINE
Journal :
Healthcare technology letters
Publication Type :
Academic Journal
Accession number :
32038871
Full Text :
https://doi.org/10.1049/htl.2019.0064