Prediction of Lung Tumor Motion Based on Recurrent Neural Network

2018 ◽  
Author(s):  
Jiang Kai ◽  
Fumitake Fujii ◽  
Takehiro Shiinoki
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Lung Tumor ◽  
Tumor Motion

On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications

2005 ◽  
Vol 32 (12) ◽  
pp. 3801-3809 ◽  
Author(s):  
Marcus Isaksson ◽  
Joakim Jalden ◽  
Martin J. Murphy
Keyword(s):  
Neural Network ◽  
Lung Tumor ◽  
Tumor Motion ◽  
Adaptive Neural Network

scholarly journals A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Ivo Bukovsky ◽  
Noriyasu Homma ◽  
Kei Ichiji ◽  
Matous Cejnek ◽  
Matous Slama ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Treatment Time ◽  
Lung Tumor ◽  
Computational Time ◽  
Tumor Motion ◽  
Dose Delivery ◽  
Neural Network Approach ◽  
Radiation Beam ◽  
Total Treatment Time

During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time.

An artificial neural network (ANN)-based lung-tumor motion predictor for intrafractional MR tumor tracking

2012 ◽  
Vol 39 (7Part1) ◽  
pp. 4423-4433 ◽  
Author(s):  
Jihyun Yun ◽  
Marc Mackenzie ◽  
Satyapal Rathee ◽  
Don Robinson ◽  
B. G. Fallone
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lung Tumor ◽  
Tumor Motion ◽  
Tumor Tracking ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

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