scholarly journals Real-Time Segmentation of Non-rigid Surgical Tools Based on Deep Learning and Tracking

2017 ◽  
pp. 84-95 ◽  
Author(s):  
Luis C. García-Peraza-Herrera ◽  
Wenqi Li ◽  
Caspar Gruijthuijsen ◽  
Alain Devreker ◽  
George Attilakos ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Surgical Tools ◽  
Time Segmentation

Real-Time Segmentation of Surgical Tools and Needle Using a Mobile-U-Net

2021 ◽  
Author(s):  
Jakob Kristian Holm Andersen ◽  
Kim Lindberg Schwaner ◽  
Thiusius Rajeeth Savarimuthu
Keyword(s):  
Real Time ◽  
Surgical Tools ◽  
Time Segmentation

Indoor localization of hand-held OCT probe using visual odometry and real-time segmentation using deep learning

2021 ◽  
pp. 1-1
Author(s):  
Xi Qin ◽  
Yu Chen ◽  
Bohan Wang ◽  
David Boegner ◽  
Brandon Gaitan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Indoor Localization ◽  
Visual Odometry ◽  
Time Segmentation

scholarly journals ToolNet: Holistically-nested real-time segmentation of robotic surgical tools

2017 ◽  
Author(s):  
Luis C. Garcia-Peraza-Herrera ◽  
Wenqi Li ◽  
Lucas Fidon ◽  
Caspar Gruijthuijsen ◽  
Alain Devreker ◽  
...  
Keyword(s):  
Real Time ◽  
Surgical Tools ◽  
Time Segmentation

Deep Learning Based Real-time Segmentation in Ultrasonic Imaging Following the Doctor's Voice Guide

2021 ◽  
Author(s):  
Fei Dai ◽  
Yifang Li ◽  
Qinzhen Shi ◽  
Xiaojun Song ◽  
Xin Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Ultrasonic Imaging ◽  
Time Segmentation

scholarly journals Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 817
Author(s):  
Alicia Pose Díez de la Lastra ◽  
Lucía García-Duarte Sáenz ◽  
David García-Mato ◽  
Luis Hernández-Álvarez ◽  
Santiago Ochandiano ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Surgical Simulation ◽  
Cranial Vault ◽  
Network Architectures ◽  
Phase Detection ◽  
Surgical Tools ◽  
3D Printed ◽  
Learning Architectures ◽  
Cranial Vault Remodeling

Deep learning is a recent technology that has shown excellent capabilities for recognition and identification tasks. This study applies these techniques in open cranial vault remodeling surgeries performed to correct craniosynostosis. The objective was to automatically recognize surgical tools in real-time and estimate the surgical phase based on those predictions. For this purpose, we implemented, trained, and tested three algorithms based on previously proposed Convolutional Neural Network architectures (VGG16, MobileNetV2, and InceptionV3) and one new architecture with fewer parameters (CranioNet). A novel 3D Slicer module was specifically developed to implement these networks and recognize surgical tools in real time via video streaming. The training and test data were acquired during a surgical simulation using a 3D printed patient-based realistic phantom of an infant’s head. The results showed that CranioNet presents the lowest accuracy for tool recognition (93.4%), while the highest accuracy is achieved by the MobileNetV2 model (99.6%), followed by VGG16 and InceptionV3 (98.8% and 97.2%, respectively). Regarding phase detection, InceptionV3 and VGG16 obtained the best results (94.5% and 94.4%), whereas MobileNetV2 and CranioNet presented worse values (91.1% and 89.8%). Our results prove the feasibility of applying deep learning architectures for real-time tool detection and phase estimation in craniosynostosis surgeries.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Real-time Worker Safety Management System Using Deep Learning-based Video Analysis Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 25-30
Author(s):  
So Yeon Jeon ◽  
Jong Hwa Park ◽  
Sang Byung Youn ◽  
Young Soo Kim ◽  
Yong Sung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Analysis ◽  
Management System ◽  
Safety Management ◽  
Worker Safety ◽  
Safety Management System ◽  
Analysis Algorithm

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

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