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Artificial Intelligence in Radiation Therapy

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Cover of 'Artificial Intelligence in Radiation Therapy'

Table of Contents

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    Book Overview
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    Chapter 1 Using Supervised Learning and Guided Monte Carlo Tree Search for Beam Orientation Optimization in Radiation Therapy
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    Chapter 2 Feasibility of CT-Only 3D Dose Prediction for VMAT Prostate Plans Using Deep Learning
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    Chapter 3 Automatically Tracking and Detecting Significant Nodal Mass Shrinkage During Head-and-Neck Radiation Treatment Using Image Saliency
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    Chapter 4 4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network
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    Chapter 5 Toward Markerless Image-Guided Radiotherapy Using Deep Learning for Prostate Cancer
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    Chapter 6 A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network
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    Chapter 7 A Novel Deep Learning Framework for Standardizing the Label of OARs in CT
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    Chapter 8 Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery
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    Chapter 9 Voxel-Level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions
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    Chapter 10 Online Target Volume Estimation and Prediction from an Interlaced Slice Acquisition - A Manifold Embedding and Learning Approach
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    Chapter 11 One-Dimensional Convolutional Network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning
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    Chapter 12 Unpaired Synthetic Image Generation in Radiology Using GANs
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    Chapter 13 Deriving Lung Perfusion Directly from CT Image Using Deep Convolutional Neural Network: A Preliminary Study
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    Chapter 14 Individualized 3D Dose Distribution Prediction Using Deep Learning
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    Chapter 15 Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy
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    Chapter 16 Dose Distribution Prediction for Optimal Treamtment of Modern External Beam Radiation Therapy for Nasopharyngeal Carcinoma
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    Chapter 17 DeepMCDose: A Deep Learning Method for Efficient Monte Carlo Beamlet Dose Calculation by Predictive Denoising in MR-Guided Radiotherapy
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    Chapter 18 UC-GAN for MR to CT Image Synthesis
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    Chapter 19 CBCT-Based Synthetic MRI Generation for CBCT-Guided Adaptive Radiotherapy
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    Chapter 20 Cardio-Pulmonary Substructure Segmentation of CT Images Using Convolutional Neural Networks
Attention for Chapter 4: 4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network
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Citations

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Chapter title
4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network
Chapter number 4
Book title
Artificial Intelligence in Radiation Therapy
Published by
Springer, Cham, October 2019
DOI 10.1007/978-3-030-32486-5_4
Book ISBNs
978-3-03-032485-8, 978-3-03-032486-5
Authors

Yang Lei, Yabo Fu, Joseph Harms, Tonghe Wang, Walter J. Curran, Tian Liu, Kristin Higgins, Xiaofeng Yang, Lei, Yang, Fu, Yabo, Harms, Joseph, Wang, Tonghe, Curran, Walter J., Liu, Tian, Higgins, Kristin, Yang, Xiaofeng

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 13%
Student > Master 2 9%
Researcher 2 9%
Professor 2 9%
Librarian 1 4%
Other 2 9%
Unknown 11 48%
Readers by discipline Count As %
Engineering 4 17%
Computer Science 3 13%
Physics and Astronomy 1 4%
Nursing and Health Professions 1 4%
Social Sciences 1 4%
Other 1 4%
Unknown 12 52%

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