Surgical-tools detection based on Convolutional Neural Network in laparoscopic robot-assisted surgery

2017 ◽  
Author(s):  
Bareum Choi ◽  
Kyungmin Jo ◽  
Songe Choi ◽  
Jaesoon Choi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Robot Assisted ◽  
Surgical Tools ◽  
Robot Assisted Surgery

scholarly journals Predicting Surgical Phases using CNN-NARX Neural Network

2019 ◽  
Vol 5 (1) ◽  
pp. 405-407
Author(s):  
Nour Aldeen Jalal ◽  
Tamer Abdulbaki Alshirbaji ◽  
Knut Möller
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Relevant Information ◽  
Surgical Tools ◽  
Use Of Data ◽  
Input Signals ◽  
Phase Prediction ◽  
Context Aware System ◽  
Laparoscopic Videos ◽  
Narx Neural Network

AbstractOnline recognition of surgical phases is essential to develop systems able to effectively conceive the workflow and provide relevant information to surgical staff during surgical procedures. These systems, known as context-aware system (CAS), are designed to assist surgeons, improve scheduling efficiency of operating rooms (ORs) and surgical team and promote a comprehensive perception and awareness of the OR. State-of-the-art studies for recognizing surgical phases have made use of data from different sources such as videos or binary usage signals from surgical tools. In this work, we propose a deep learning pipeline, namely a convolutional neural network (CNN) and a nonlinear autoregressive network with exogenous inputs (NARX), designed to predict surgical phases from laparoscopic videos. A convolutional neural network (CNN) is used to perform the tool classification task by automatically learning visual features from laparoscopic videos. The output of the CNN, which represents binary usage signals of surgical tools, is provided to a NARX neural network that performs a multistep-ahead predictions of surgical phases. Surgical phase prediction performance of the proposed pipeline was evaluated on a dataset of 80 cholecystectomy videos (Cholec80 dataset). Results show that the NARX model provides a good modelling of the temporal dependencies between surgical phases. However, more input signals are needed to improve the recognition accuracy.

scholarly journals Surgical Tool Classification in Laparoscopic Videos Using Convolutional Neural Network

2018 ◽  
Vol 4 (1) ◽  
pp. 407-410 ◽  
Author(s):  
Tamer Abdulbaki Alshirbaji ◽  
Nour Aldeen Jalal ◽  
Knut Möller
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Classification Performance ◽  
Training Data ◽  
Negative Effects ◽  
Surgical Tools ◽  
Surgical Tool ◽  
Context Aware System ◽  
Source Of Information ◽  
Laparoscopic Videos

AbstractLaparoscopic videos are a very important source of information which is inherently available in minimally invasive surgeries. Detecting surgical tools based on that videos have gained increasing interest due to its importance in developing a context-aware system. Such system can provide guidance assistance to the surgical team and optimise the processes inside the operating room. Convolutional neural network is a robust method to learn discriminative visual features and classify objects. As it expects a uniform distribution of data over classes, it fails to identify classes which are under-presented in the training data. In this work, loss-sensitive learning approach and resampling techniques were applied to counter the negative effects of imbalanced laparoscopic data on training the CNN model. The obtained results showed improvement in the classification performance especially for detecting surgical tools which are shortly used in the procedure.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network
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