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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

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Dan Nguyen

    1. The University of Texas Southwestern Medical Center, Dallas, USA

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  2. Lei Xing

    1. Stanford University, Stanford, USA

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  3. Steve Jiang

    1. The University of Texas Southwestern Medical Center, Dallas, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11850)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: AIRT 2019.

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Table of contents (20 papers)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. Using Supervised Learning and Guided Monte Carlo Tree Search for Beam Orientation Optimization in Radiation Therapy

    • Azar Sadeghnejad Barkousaraie, Olalekan Ogunmolu, Steve Jiang, Dan Nguyen
    Pages 1-9

  3. Feasibility of CT-Only 3D Dose Prediction for VMAT Prostate Plans Using Deep Learning

    • Siri Willems, Wouter Crijns, Edmond Sterpin, Karin Haustermans, Frederik Maes
    Pages 10-17

  4. Automatically Tracking and Detecting Significant Nodal Mass Shrinkage During Head-and-Neck Radiation Treatment Using Image Saliency

    • Yu-chi Hu, Cynthia Polvorosa, Chiaojung Jillian Tsai, Margie Hunt
    Pages 18-25

  5. 4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network

    • Yang Lei, Yabo Fu, Joseph Harms, Tonghe Wang, Walter J. Curran, Tian Liu et al.
    Pages 26-33

  6. Toward Markerless Image-Guided Radiotherapy Using Deep Learning for Prostate Cancer

    • Wei Zhao, Bin Han, Yong Yang, Mark Buyyounouski, Steven L. Hancock, Hilary Bagshaw et al.
    Pages 34-42

  7. A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network

    • Zhiyu Liu, Wenhao Jiang, Kit-Hang Lee, Yat-Long Lo, Yui-Lun Ng, Qi Dou et al.
    Pages 43-51

  8. A Novel Deep Learning Framework for Standardizing the Label of OARs in CT

    • Qiming Yang, Hongyang Chao, Dan Nguyen, Steve Jiang
    Pages 52-60

  9. Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery

    • Szu-Yeu Hu, Wei-Hung Weng, Shao-Lun Lu, Yueh-Hung Cheng, Furen Xiao, Feng-Ming Hsu et al.
    Pages 61-69

  10. Voxel-Level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions

    • Jingjing Zhang, Shuolin Liu, Teng Li, Ronghu Mao, Chi Du, Jianfei Liu
    Pages 70-77

  11. Online Target Volume Estimation and Prediction from an Interlaced Slice Acquisition - A Manifold Embedding and Learning Approach

    • John Ginn, James Lamb, Dan Ruan
    Pages 78-85

  12. One-Dimensional Convolutional Network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning

    • Dashan Jiang, Teng Li, Ronghu Mao, Chi Du, Yongbin Liu, Shuolin Liu et al.
    Pages 86-93

  13. Unpaired Synthetic Image Generation in Radiology Using GANs

    • Denis Prokopenko, Joël Valentin Stadelmann, Heinrich Schulz, Steffen Renisch, Dmitry V. Dylov
    Pages 94-101

  14. Deriving Lung Perfusion Directly from CT Image Using Deep Convolutional Neural Network: A Preliminary Study

    • Ge Ren, Wai Yin Ho, Jing Qin, Jing Cai
    Pages 102-109

  15. Individualized 3D Dose Distribution Prediction Using Deep Learning

    • Jianhui Ma, Ti Bai, Dan Nguyen, Michael Folkerts, Xun Jia, Weiguo Lu et al.
    Pages 110-118

  16. Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy

    • Kibrom Berihu Girum, Gilles Créhange, Raabid Hussain, Paul Michael Walker, Alain Lalande
    Pages 119-127

  17. Dose Distribution Prediction for Optimal Treamtment of Modern External Beam Radiation Therapy for Nasopharyngeal Carcinoma

    • Bilel Daoud, Ken’ichi Morooka, Shoko Miyauchi, Ryo Kurazume, Wafa Mnejja, Leila Farhat et al.
    Pages 128-136

  18. DeepMCDose: A Deep Learning Method for Efficient Monte Carlo Beamlet Dose Calculation by Predictive Denoising in MR-Guided Radiotherapy

    • Ryan Neph, Yangsibo Huang, Youming Yang, Ke Sheng
    Pages 137-145

  19. UC-GAN for MR to CT Image Synthesis

    • Haitao Wu, Xiling Jiang, Fucang Jia
    Pages 146-153

  20. CBCT-Based Synthetic MRI Generation for CBCT-Guided Adaptive Radiotherapy

    • Yang Lei, Tonghe Wang, Joseph Harms, Yabo Fu, Xue Dong, Walter J. Curran et al.
    Pages 154-161
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  1. Artificial Intelligence in Radiation Therapy

Keywords

About this book

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.

Editors and Affiliations

  • The University of Texas Southwestern Medical Center, Dallas, USA

    Dan Nguyen, Steve Jiang

  • Stanford University, Stanford, USA

    Lei Xing

Bibliographic Information

  • Book Title: Artificial Intelligence in Radiation Therapy

  • Book Subtitle: First International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings

  • Editors: Dan Nguyen, Lei Xing, Steve Jiang

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-32486-5

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2019

  • Softcover ISBN: 978-3-030-32485-8Published: 18 October 2019

  • eBook ISBN: 978-3-030-32486-5Published: 10 October 2019

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XI, 172

  • Number of Illustrations: 13 b/w illustrations, 74 illustrations in colour

  • Topics: Image Processing and Computer Vision, Artificial Intelligence, Health Informatics

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