Review of research on detection and tracking of minimally invasive surgical tools based on deep learning.
10.7507/1001-5515.201904061
- Author:
Yuying LIU
1
;
Zijian ZHAO
1
Author Information
1. School of Control Science and Engineering, Shandong University, Jinan 250061, P.R.China.
- Publication Type:Journal Article
- Keywords:
convolutional neural network;
deep learning;
fully supervised;
surgical tool detection and tracking;
weakly supervised
- MeSH:
Algorithms;
Deep Learning;
Minimally Invasive Surgical Procedures;
instrumentation;
Neural Networks (Computer)
- From:
Journal of Biomedical Engineering
2019;36(5):870-878
- CountryChina
- Language:Chinese
-
Abstract:
The application of minimally invasive surgical tool detection and tracking technology based on deep learning in minimally invasive surgery is currently a research hotspot. This paper firstly expounds the relevant technical content of the minimally invasive surgery tool detection and tracking, which mainly introduces the advantages based on deep learning algorithm. Then, this paper summarizes the algorithm for detection and tracking surgical tools based on fully supervised deep neural network and the emerging algorithm for detection and tracking surgical tools based on weakly supervised deep neural network. Several typical algorithm frameworks and their flow charts based on deep convolutional and recurrent neural networks are summarized emphatically, so as to enable researchers in relevant fields to understand the current research progress more systematically and provide reference for minimally invasive surgeons to select navigation technology. In the end, this paper provides a general direction for the further research of minimally invasive surgical tool detection and tracking technology based on deep learning.
