scholarly journals A Study on Stereoscopic X-ray Imaging Data Set on the Accuracy of Real-Time Tumor Tracking in External Beam Radiotherapy

2016 ◽  
Vol 16 (2) ◽  
pp. 167-177 ◽  
Author(s):  
Ahmad Esmaili Torshabi ◽  
Leila Ghorbanzadeh
Keyword(s):  
External Beam Radiotherapy ◽  
Training Data ◽  
Imaging Data ◽  
Data Set ◽  
X Ray ◽  
Tumor Tracking ◽  
X Ray Imaging ◽  
Additional Imaging ◽  
Data Points ◽  
Imaging Dose

At external beam radiotherapy, stereoscopic X-ray imaging system is responsible as tumor motion information provider. This system takes X-ray images intermittently from tumor position (1) at pretreatment step to provide training data set for model construction and (2) during treatment to control the accuracy of correlation model performance. In this work, we investigated the effect of imaging data points provided by this system on treatment quality. Because some information is still lacking about (1) the number of imaging data points, (2) shooting time for capturing each data point, and also (3) additional imaging dose delivered by this system. These 3 issues were comprehensively assessed at (1) pretreatment step while training data set is gathered for prediction model construction and (2) during treatment while model is tested and reconstructed using new arrival data points. A group of real patients treated with CyberKnife Synchrony module was chosen in this work, and an adaptive neuro-fuzzy inference system was considered as consistent correlation model. Results show that a proper model can be constructed while the number of imaging data points is highly enough to represent a good pattern of breathing cycles. Moreover, a trade-off between the number of imaging data points and additional imaging dose is considered in this study. Since breathing phenomena are highly variable at different patients, the time for taking some of imaging data points is very important, while their absence at that critical time may yield wrong tumor tracking. In contrast, the sensitivity of another category of imaging data points is not high, while breathing is normal and in the control range. Therefore, an adaptive supervision on the implementation of stereoscopic X-ray imaging is proposed to intelligently accomplish shooting process, based on breathing motion variations.

scholarly journals 2D–3D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryoya Shiode ◽  
Mototaka Kabashima ◽  
Yuta Hiasa ◽  
Kunihiro Oka ◽  
Tsuyoshi Murase ◽  
...  
Keyword(s):  
Wrist Joint ◽  
High Accuracy ◽  
Training Data ◽  
Small Data ◽  
Data Set ◽  
Accuracy Estimation ◽  
X Ray ◽  
Learning Network ◽  
Forearm Bone ◽  
Deep Learning Network

AbstractThe purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.

An Incremental Isomap Method for Hyperspectral Dimensionality Reduction and Classification

2021 ◽  
Vol 87 (6) ◽  
pp. 445-455
Author(s):  
Yi Ma ◽  
Zezhong Zheng ◽  
Yutang Ma ◽  
Mingcang Zhu ◽  
Ran Huang ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Training Data ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Data Points

Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N2). We pres- ent in this article an incremental manifold learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature varia- tion algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support vector machine.

scholarly journals Using dual-energy x-ray imaging to enhance automated lung tumor tracking during real-time adaptive radiotherapy

2015 ◽  
Vol 42 (12) ◽  
pp. 6987-6998 ◽  
Author(s):  
Martin J. Menten ◽  
Martin F. Fast ◽  
Simeon Nill ◽  
Uwe Oelfke
Keyword(s):  
Real Time ◽  
Lung Tumor ◽  
Dual Energy ◽  
Adaptive Radiotherapy ◽  
X Ray ◽  
Tumor Tracking ◽  
X Ray Imaging

scholarly journals MA13.01 Markerless Tumor Tracking during Lung Radiotherapy Using Intrafraction X-Ray Imaging

2017 ◽  
Vol 12 (1) ◽  
pp. S415-S416
Author(s):  
Chun-Chien (Andy) Shieh ◽  
Vincent Caillet ◽  
Michelle Dunbar ◽  
Paul Keall ◽  
Nicholas Hardcastle ◽  
...  
Keyword(s):  
X Ray ◽  
Tumor Tracking ◽  
X Ray Imaging ◽  
Lung Radiotherapy

scholarly journals Transfer learning to detect COVID-19 automatically from X-ray images, using convolutional neural networks

2020 ◽  
Author(s):  
Mundher Taresh ◽  
Ningbo Zhu ◽  
Talal Ahmed Ali Ali
Keyword(s):  
Deep Learning ◽  
Confusion Matrix ◽  
High Accuracy ◽  
Classification Model ◽  
Statistical Parameters ◽  
Data Set ◽  
X Ray ◽  
X Ray Imaging ◽  
Chest X Ray ◽  
Novel Coronavirus

AbstractNovel coronavirus pneumonia (COVID-19) is a contagious disease that has already caused thousands of deaths and infected millions of people worldwide. Thus, all technological gadgets that allow the fast detection of COVID-19 infection with high accuracy can offer help to healthcare professionals. This study is purposed to explore the effectiveness of artificial intelligence (AI) in the rapid and reliable detection of COVID-19 based on chest X-ray imaging. In this study, reliable pre-trained deep learning algorithms were applied to achieve the automatic detection of COVID-19-induced pneumonia from digital chest X-ray images.Moreover, the study aims to evaluate the performance of advanced neural architectures proposed for the classification of medical images over recent years. The data set used in the experiments involves 274 COVID-19 cases, 380 viral pneumonia, and 380 healthy cases, which was collected from the available X-ray images on public medical repositories. The confusion matrix provided a basis for testing the post-classification model. Furthermore, an open-source library PyCM* was used to support the statistical parameters. The study revealed the superiority of Model VGG16 over other models applied to conduct this research where the model performed best in terms of overall scores and based-class scores. According to the research results, deep learning with X-ray imaging is useful in the collection of critical biological markers associated with COVID-19 infection. The technique is conducive for the physicians to make a diagnosis of COVID-19 infection. Meanwhile, the high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis.

WE-A-203-01: X-Ray Imaging Dose from IGRT

2010 ◽  
Vol 37 (6Part15) ◽  
pp. 3409-3409 ◽  
Author(s):  
G Ding
Keyword(s):  
X Ray ◽  
X Ray Imaging ◽  
Imaging Dose
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