Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks

Nyflot Matthew J.; Thammasorn Phawis; Wootton Landon S.; Ford Eric C.; Chaovalitwongse W. Art

首页> 外文期刊>Medical Physics >Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks

【24h】

Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks

机译：患者特异性质量保证的深度学习：通过卷积神经网络的伽马图像射频分析识别放射疗法递送的误差

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

PurposePatient-specific quality assurance (QA) for intensity-modulated radiation therapy (IMRT) is a ubiquitous clinical procedure, but conventional methods have often been criticized as being insensitive to errors or less effective than other common physics checks. Recently, there has been interest in the application of radiomics, quantitative extraction of image features, to radiotherapy QA. In this work, we investigate a deep learning approach to classify the presence or absence of introduced radiotherapy treatment delivery errors from patient-specific QA.

机译：针对强度调制的放射治疗（IMRT）的Purposepatient特定的质量保证（QA）是一种普遍存在的临床程序，但常规方法通常被批评为对其他常见物理检查的错误或更少有效的误差不敏感。最近，对放射性物质的施用，对图像特征的定量提取，放射治疗QA有兴趣。在这项工作中，我们调查了深入的学习方法来分类来自患者特异性QA引入的放射治疗递送误差的存在或不存在。

著录项

来源
《Medical Physics》 |2019年第2期|共9页
作者
Nyflot Matthew J.; Thammasorn Phawis; Wootton Landon S.; Ford Eric C.; Chaovalitwongse W. Art;
展开▼
作者单位

Univ Washington Dept Radiat Oncol Seattle WA 98195 USA;

Univ Arkansas Dept Ind Engn Fayetteville AR 72701 USA;

Univ Washington Dept Radiat Oncol Seattle WA 98195 USA;

Univ Washington Dept Radiat Oncol Seattle WA 98195 USA;

Univ Washington Dept Radiol Seattle WA 98195 USA;

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类基础医学;
关键词
deep learning; IMRT QA; quality assurance; radiomics; texture features;

机译：深入学习;IMRT QA;质量保证;辐射瘤;纹理特征;

相似文献

外文文献
中文文献
专利

1. Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks [J] . Nyflot Matthew J., Thammasorn Phawis, Wootton Landon S., Medical Physics . 2019,第2期

机译：患者特异性质量保证的深度学习：通过卷积神经网络的伽马图像射频分析识别放射疗法递送的误差
2. Prediction of IMRT/VMAT Quality Assurance Gamma Analysis Passing Rates Using Convolutional Neural Networks [J] . Ziemer B., Iterian Y., Solberg T., Medical Physics . 2018,第6期

机译：使用卷积神经网络预测IMRT / VMAT质量保证伽马分析通过率
3. Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study [J] . Hsiang-Chun Dong, Hsiang-Kai Dong, Mu-Hsien Yu, International Journal of Environmental Research and Public Health . 2020,第16期

机译：利用MR图像使用深度学习卷积神经网络方法来识别肌瘤中肌瘤内膜癌的侵袭深度：试验研究
4. DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK APPLICATIONS IN IMAGE ANALYSIS TO IDENTIFY DEFECTS [C] . Srijith P Umakanth, Sabri Cetin International Symposium on Test Automation Instrumentation . 2018

机译：深度学习卷积神经网络在图像分析中识别缺陷的应用
5. Deep Learning Convolutional Neural Network Applications in Image Analysis to Identify Defects [D] . Umakanth, Srijith Parakkat 2018

机译：深度学习卷积神经网络在图像分析中识别缺陷的应用
6. Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study [O] . Hsiang-Chun Dong, Hsiang-Kai Dong, Mu-Hsien Yu, 2020

机译：利用MR图像使用深度学习卷积神经网络方法来识别肌瘤中肌瘤内膜癌的侵袭深度：试验研究
7. Identification of Images of COVID-19 from Chest X-rays Using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0™ Software with Open Source Convolutional Neural Networks [O] . Arjun Sarkar, Joerg Vandenhirtz, Jozsef Nagy, 2021

机译：利用深度学习识别Covid-19的胸部X光图像：比较康涅克斯VisionPro深度学习1.0™软件与开源卷积神经网络

Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks

摘要

著录项

相似文献

相关主题

期刊订阅