PH-0265 Diaphragm motion prediction based on optical surface with machine learning for liver tumor SBRT

2021 ◽  
Vol 161 ◽  
pp. S173-S174
Author(s):  
Z. Dai ◽  
Y. Zhang ◽  
Q. He ◽  
S. Zhao ◽  
Y. Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Tumor ◽  
Motion Prediction ◽  
Optical Surface ◽  
Diaphragm Motion

scholarly journals Active learning of deep surrogates for PDEs: application to metasurface design

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Raphaël Pestourie ◽  
Youssef Mroueh ◽  
Thanh V. Nguyen ◽  
Payel Das ◽  
Steven G. Johnson
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Engineering Optimization ◽  
Optical Surface ◽  
Optical Wavelength ◽  
Random Samples ◽  
Training Cost ◽  
Order Of Magnitude

Abstract Surrogate models for partial differential equations are widely used in the design of metamaterials to rapidly evaluate the behavior of composable components. However, the training cost of accurate surrogates by machine learning can rapidly increase with the number of variables. For photonic-device models, we find that this training becomes especially challenging as design regions grow larger than the optical wavelength. We present an active-learning algorithm that reduces the number of simulations required by more than an order of magnitude for an NN surrogate model of optical-surface components compared to uniform random samples. Results show that the surrogate evaluation is over two orders of magnitude faster than a direct solve, and we demonstrate how this can be exploited to accelerate large-scale engineering optimization.

scholarly journals Supplementary material to "Ground motion prediction maps using seismic microzonation data and machine learning"

2021 ◽  
Author(s):  
Federico Mori ◽  
Amerigo Mendicelli ◽  
Gaetano Falcone ◽  
Gianluca Acunzo ◽  
Rose Line Spacagna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ground Motion ◽  
Seismic Microzonation ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Supplementary Material

scholarly journals Classification of Liver Tumor on Ct Images using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 11336-11338
Keyword(s):  
Machine Learning ◽  
Liver Tumor ◽  
Region Of Interest ◽  
Region Growing ◽  
Ct Images ◽  
Svm Classifier ◽  
Intensity Level ◽  
Image Region ◽  
Occurrence Matrix

Liver tumor is one of the most severe types of cancerous diseases which is responsible for the death of many patients. CT Liver tumor images have more noises which is difficult to diagnose the level of the tumor. It is a challenging task to automatically identify the tumor from CT images because of several anatomical changes in different patients. The tumor is difficult to find because of the presence of objects with same intensity level. In this proposed system, fully automated machine learning is used to detect the liver tumor from CT image. Region growing technique is used to segment the region of interest. The textural feature are extracted from Gray level co-occurrence matrix (GLCM) of the segmented image. Extracted textural features are given as input to the designed SVM classifier system. Performance analysis of SVM classification of CT liver tumor image is studied. This will be useful for physician in better automatic diagnosis of liver tumor from CT images.

scholarly journals Ground motion prediction maps using seismic microzonation data and machine learning

2021 ◽  
Author(s):  
Federico Mori ◽  
Amerigo Mendicelli ◽  
Gaetano Falcone ◽  
Gianluca Acunzo ◽  
Rose Line Spacagna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Ground Motion ◽  
Scale Effects ◽  
Earthquake Ground Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Intensity Measures ◽  
Local Site ◽  
Ground Motion Variability

Abstract. Past seismic events worldwide demonstrated that damage and death toll depend on both the strong ground motion (i.e., source effects) and the local site effects. The variability of earthquake ground motion distribution is caused by local stratigraphic and/or topographic setting and buried morphologies, that can give rise to amplification and resonances with respect to the ground motion expected at the reference site. Therefore, local site conditions can affect an area with damage related to the full collapse or loss in functionality of facilities, roads, pipelines, and other lifelines. To this concern, the near real time prediction of damage pattern over large areas is a crucial issue to support the rescue and operational interventions. A machine learning approach was adopted to produce ground motion prediction maps considering both stratigraphic and morphological conditions. A set of about 16'000 accelometric data and about 46'000 geological and geophysical data were retrieved from Italian and European databases. The intensity measures of interest were estimated based on 9 input proxies. The adopted machine learning regression model (i.e., Gaussian Process Regression) allows to improve both the precision and the accuracy in the estimation of the intensity measures with respect to the available near real time predictions methods (i.e., Ground Motion Prediction Equation and shaking maps). In addition, maps with a 50 × 50 m resolution were generated providing a ground motion variability in agreement with the results of advanced numerical simulations based on detailed sub-soil models. The variability at short distances (hundreds of meters) was demonstrated to be responsible for 30–40 % of the total variability of the predicted IM maps, making it desirable that seismic hazard maps also consider short-scale effects.

scholarly journals A Super-Learner Model for Tumor Motion Prediction and Management in Radiation Therapy: Development and Feasibility Evaluation

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hui Lin ◽  
Wei Zou ◽  
Taoran Li ◽  
Steven J. Feigenberg ◽  
Boon-Keng K. Teo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Free Breathing ◽  
Motion Prediction ◽  
Imaging Features ◽  
Tumor Motion ◽  
Large Tumor ◽  
Motion Management ◽  
Active Motion ◽  
Learner Model ◽  
Super Learner

Abstract In cancer radiation therapy, large tumor motion due to respiration can lead to uncertainties in tumor target delineation and treatment delivery, thus making active motion management an essential step in thoracic and abdominal tumor treatment. In current practice, patients with tumor motion may be required to receive two sets of CT scans – the initial free-breathing 4-dimensional CT (4DCT) scan for tumor motion estimation and a second CT scan under appropriate motion management such as breath-hold or abdominal compression. The aim of this study is to assess the feasibility of a predictive model for tumor motion estimation in three-dimensional space based on machine learning algorithms. The model was developed based on sixteen imaging features extracted from non-4D diagnostic CT images and eleven clinical features extracted from the Electronic Health Record (EHR) database of 150 patients to characterize the lung tumor motion. A super-learner model was trained to combine four base machine learning models including the Random Forest, Multi-Layer Perceptron, LightGBM and XGBoost, the hyper-parameters of which were also optimized to obtain the best performance. The outputs of the super-learner model consist of tumor motion predictions in the Superior-Inferior (SI), Anterior-Posterior (AP) and Left-Right (LR) directions, and were compared against tumor motions measured in the free-breathing 4DCT scans. The accuracy of predictions was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) through ten rounds of independent tests. The MAE and RMSE of predictions in the SI direction were 1.23 mm and 1.70 mm; the MAE and RMSE of predictions in the AP direction were 0.81 mm and 1.19 mm, and the MAE and RMSE of predictions in the LR direction were 0.70 mm and 0.95 mm. In addition, the relative feature importance analysis demonstrated that the imaging features are of great importance in the tumor motion prediction compared to the clinical features. Our findings indicate that a super-learner model can accurately predict tumor motion ranges as measured in the 4DCT, and could provide a machine learning framework to assist radiation oncologists in determining the active motion management strategy for patients with large tumor motion.

scholarly journals Is Diaphragm Motion a Good Surrogate for Liver Tumor Motion?

2014 ◽  
Vol 90 (4) ◽  
pp. 952-958 ◽  
Author(s):  
Juan Yang ◽  
Jing Cai ◽  
Hongjun Wang ◽  
Zheng Chang ◽  
Brian G. Czito ◽  
...  
Keyword(s):  
Liver Tumor ◽  
Tumor Motion ◽  
Diaphragm Motion
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