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Artificial intelligence in radiation...

AIRT (Workshop) (2019 :)

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Artificial intelligence in radiation therapy = first International Workshop, AIRT 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial intelligence in radiation therapy/ edited by Dan Nguyen, Lei Xing, Steve Jiang.
其他題名:	first International Workshop, AIRT 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
其他題名:	AIRT 2019
其他作者:	Nguyen, Dan.
團體作者:	AIRT (Workshop)
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xi, 172 p. :ill. (some col.), digital ;24 cm.
內容註:	Using Supervised Learning and Guided Monte Carlo Tree Search for Beam Orientation Optimization in Radiation Therapy -- Feasibility of CT-only 3D dose prediction for VMAT prostate plans using deep learning -- Automatically Tracking and Detecting Signiﬁcant Nodal Mass Shrinkage During Head-and-Neck Radiation Treatment Using Image Saliency -- 4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network -- Toward markerless image-guided radiotherapy using deep learning for prostate cancer -- A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network -- A Novel Deep Learning Framework for Standardizing the Label of OARs in CT -- Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery -- Voxel-level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions -- Online Target Volume Estimation and Prediction From an Interlaced Slice Acquisition - A Manifold Embedding and Learning Approach -- One-dimensional convolutional network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning -- Unpaired Synthetic Image Generation in Radiology Using GANs -- Deriving lung perfusion directly from CT image using deep convolutional neural network: A preliminary study -- Individualized 3D Dose Distribution Prediction Using Deep Learning -- Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy -- Dose Distribution Prediction for Optimal Treatment of Modern External Beam Radiation Therapy for Nasopharyngeal Carcinoma -- DeepMCDose: A Deep Learning Method for Efficient Monte Carlo Beamlet Dose Calculation by Predictive Denoising in MR-Guided Radiotherapy -- UC-GAN for MR to CT Image Synthesis -- CBCT-based Synthetic MRI Generation for CBCT-guided Adaptive Radiotherapy -- Cardio-pulmonary Substructure Segmentation of CT images using Convolutional Neural Networks.
Contained By:	Springer eBooks
標題:	Artificial intelligence - Medical applications -
電子資源:	https://doi.org/10.1007/978-3-030-32486-5
ISBN:	9783030324865

Artificial intelligence in radiation therapy = first International Workshop, AIRT 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
Artificial intelligence in radiation therapyfirst International Workshop, AIRT 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /[electronic resource] :AIRT 2019edited by Dan Nguyen, Lei Xing, Steve Jiang. - Cham :Springer International Publishing :2019. - xi, 172 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,118500302-9743 ;. - Lecture notes in computer science ;11850..

Using Supervised Learning and Guided Monte Carlo Tree Search for Beam Orientation Optimization in Radiation Therapy -- Feasibility of CT-only 3D dose prediction for VMAT prostate plans using deep learning -- Automatically Tracking and Detecting Signiﬁcant Nodal Mass Shrinkage During Head-and-Neck Radiation Treatment Using Image Saliency -- 4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network -- Toward markerless image-guided radiotherapy using deep learning for prostate cancer -- A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network -- A Novel Deep Learning Framework for Standardizing the Label of OARs in CT -- Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery -- Voxel-level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions -- Online Target Volume Estimation and Prediction From an Interlaced Slice Acquisition - A Manifold Embedding and Learning Approach -- One-dimensional convolutional network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning -- Unpaired Synthetic Image Generation in Radiology Using GANs -- Deriving lung perfusion directly from CT image using deep convolutional neural network: A preliminary study -- Individualized 3D Dose Distribution Prediction Using Deep Learning -- Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy -- Dose Distribution Prediction for Optimal Treatment of Modern External Beam Radiation Therapy for Nasopharyngeal Carcinoma -- DeepMCDose: A Deep Learning Method for Efficient Monte Carlo Beamlet Dose Calculation by Predictive Denoising in MR-Guided Radiotherapy -- UC-GAN for MR to CT Image Synthesis -- CBCT-based Synthetic MRI Generation for CBCT-guided Adaptive Radiotherapy -- Cardio-pulmonary Substructure Segmentation of CT images using Convolutional Neural Networks.

This book constitutes the refereed proceedings of the First International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 20 full papers presented were carefully reviewed and selected from 24 submissions. The papers discuss the state of radiation therapy, the state of AI and related technologies, and hope to find a pathway to revolutionizing the field to ultimately improve cancer patient outcome and quality of life.

ISBN: 9783030324865

Standard No.: 10.1007/978-3-030-32486-5doiSubjects--Topical Terms:

660945
Artificial intelligence
--Medical applications

LC Class. No.: R859.7.A78 / A57 2019

Dewey Class. No.: 610.28563

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