Artificial Intelligence in Radiation Therapy: First International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings

182 Pages·2019·23.114 MB·English

by  Dan Nguyen, Lei Xing, Steve Jiang

Dan Nguyen
Lei Xing
Steve Jiang (Eds.)
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First International Workshop, AIRT 2019
Held in Conjunction with MICCAI 2019
Shenzhen, China, October 17, 2019
Proceedings
Lecture Notes in Computer Science 11850
Founding Editors
Gerhard Goos
Karlsruhe Institute of Technology, Karlsruhe, Germany
Juris Hartmanis
Cornell University, Ithaca, NY, USA
Editorial Board Members
Elisa Bertino
Purdue University, West Lafayette, IN, USA
Wen Gao
Peking University, Beijing, China
Bernhard Steffen
TU Dortmund University, Dortmund, Germany
Gerhard Woeginger
RWTH Aachen, Aachen, Germany
Moti Yung
Columbia University, New York, NY, USA
More information about this series at http://www.springer.com/series/7412
Dan Nguyen Lei Xing Steve Jiang (Eds.)
(cid:129) (cid:129)
fi
Arti cial Intelligence
in Radiation Therapy
First International Workshop, AIRT 2019
Held in Conjunction with MICCAI 2019
Shenzhen, China, October 17, 2019
Proceedings
123
Editors
Dan Nguyen LeiXing
TheUniversity of Texas StanfordUniversity
Southwestern Medical Center Stanford, CA,USA
Dallas, TX,USA
SteveJiang
TheUniversity of Texas
Southwestern Medical Center
Dallas, TX,USA
ISSN 0302-9743 ISSN 1611-3349 (electronic)
Lecture Notesin Computer Science
ISBN 978-3-030-32485-8 ISBN978-3-030-32486-5 (eBook)
https://doi.org/10.1007/978-3-030-32486-5
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Preface
We are pleased to present the proceedings to the First International Workshop for
Artificial Intelligence in Radiation Therapy (AIRT 2019), which took place on
October17,2019,andwasheldinconjunctionwiththe22ndInternationalConference
onMedicalImageComputingandComputerAssistedIntervention(MICCAI2019),in
Shenzhen, China, during October 13–17, 2019.
This workshop included 20 accepted presentations featuring the most recent work
focusedontheapplicationofartificialintelligence(AI)andautomationtechnologiesin
radiationtherapy.Withthisworkshop,wehopetoopenadiscussionaboutthestateof
radiation therapy, the state of AI and related technologies, and pave the way to
revolutionizing the field to ultimately improve cancer patient outcome and quality of
life. We believe that in working with the intelligent minds at MICCAI, the field of
radiation therapy will greatly benefit from the exposure of the latest cutting-edge
algorithms, and MICCAI will grow from tackling the unique challenges in radiation
therapy.
In particular, we will focus on the application and development of AI and related
technologies in two fronts: (1) image guided treatment delivery and (2) image guided
treatment strategy. Image guided treatment delivery will be focused on advancements
oftechnologiesthatareusedduringthedeliveryoftheradiationtothepatientforimage
guided radiation therapy (IGRT), which includes developments in cone beam
computed tomography (CBCT), fluoroscopy, surface imaging, motion management,
and other modalities that are used for IGRT. Image guided treatment strategy will
involve technologies that are used in the clinical pipeline leading up to the delivery,
which include segmentation techniques and algorithms on CT, MRI, and/or PET,
treatment planning, dose calculation, quality assurance and error detection, etc.
CBCT, fluoroscopy, surface imaging, and related submissions for image guided
treatment delivery focus on the use of the imaging modalities for accurate and precise
delivery of the planned radiation dose onto the tumor and healthy tissue. Motion
management includes immobilization methods and imaging for motion verification or
prediction.Segmentationrelatedsubmissionsfocusonthesegmentationthatisspecific
to the radiotherapy pipeline, and may use CT, MRI, and/or PET images for algorithm
development. Treatmentplanningsubmissions focus ontechniquesand algorithms for
improving the plan quality and/or the planning efficiency. Dose calculation related
submissions focus on photon, electron, protons, or heavy ion, with applications to
radiationtherapy.Quality assurance anderrordetectionsubmissionsrelatetoensuring
thatthecalculateddosematchesthedelivereddose,identifyinghumanmistakesduring
treatment planning and delivery, incident learning, risk analysis, and process control.
We employed the EasyChair1 conference management system for our paper
submissionsandpeerreviewprocess.Anyidentifyinginformationwasredactedinthe
1 https://easychair.org/
vi Preface
submission prior to review to maintain an anonymous review process. In total, 24 full
submissions were received and the overall acceptance rate was 83.3%. The accepted
papers have been compiled into a volume of Lecture Notes in Computer Science
(LNCS) proceedings—Volume LNCS 11850.
We would like to thank everyone who contributed greatly to the success of AIRT
2019 and the quality of its proceedings, especially the authors, co-authors, students,
and supervisors, for submitting and presenting their exceptional work to the AIRT
workshop. We believe that this workshop for AI in radiation therapy is the perfect
platform for providing discussion of the state of radiation therapy, the state of AI and
related technologies, and will pave the way to revolutionizing the field to ultimately
improve cancer patient outcome and quality of life.
September 2019 Dan Nguyen
Lei Xing
Steve Jiang
Organization
Organizing Committee
Dan Nguyen MedicalArtificialIntelligenceandAutomation(MAIA)
Laboratory, Department of Radiation Oncology,
UT Southwestern Medical Center, USA
Lei Xing Laboratory for Artificial Intelligence in Medicine
and Biomedical Physics, Department of Radiation
Oncology, Stanford Medicine, USA
Steve Jiang MedicalArtificialIntelligenceandAutomation(MAIA)
Laboratory, Department of Radiation Oncology,
UT Southwestern Medical Center, USA
Contents
Using Supervised Learning and Guided Monte Carlo Tree Search
for Beam Orientation Optimization in Radiation Therapy. . . . . . . . . . . . . . . 1
Azar Sadeghnejad Barkousaraie, Olalekan Ogunmolu, Steve Jiang,
and Dan Nguyen
Feasibility of CT-Only 3D Dose Prediction for VMAT Prostate Plans
Using Deep Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Siri Willems, Wouter Crijns, Edmond Sterpin, Karin Haustermans,
and Frederik Maes
Automatically Tracking and Detecting Significant Nodal Mass Shrinkage
During Head-and-Neck Radiation Treatment Using Image Saliency. . . . . . . . 18
Yu-chiHu,CynthiaPolvorosa,ChiaojungJillianTsai,andMargieHunt
4D-CT Deformable Image Registration Using an Unsupervised Deep
Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Yang Lei, Yabo Fu, Joseph Harms, Tonghe Wang, Walter J. Curran,
Tian Liu, Kristin Higgins, and Xiaofeng Yang
Toward Markerless Image-Guided Radiotherapy Using Deep Learning
for Prostate Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Wei Zhao, Bin Han, Yong Yang, Mark Buyyounouski,
Steven L. Hancock, Hilary Bagshaw, and Lei Xing
A Two-Stage Approach for Automated Prostate Lesion Detection
and Classification with Mask R-CNN and Weakly Supervised Deep
Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Zhiyu Liu, Wenhao Jiang, Kit-Hang Lee, Yat-Long Lo, Yui-Lun Ng,
Qi Dou, Varut Vardhanabhuti, and Ka-Wai Kwok
A Novel Deep Learning Framework for Standardizing the Label
of OARs in CT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Qiming Yang, Hongyang Chao, Dan Nguyen, and Steve Jiang
Multimodal Volume-Aware Detection and Segmentation for Brain
Metastases Radiosurgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Szu-Yeu Hu, Wei-Hung Weng, Shao-Lun Lu, Yueh-Hung Cheng,
Furen Xiao, Feng-Ming Hsu, and Jen-Tang Lu
x Contents
Voxel-Level Radiotherapy Dose Prediction Using Densely Connected
Network with Dilated Convolutions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
Jingjing Zhang, Shuolin Liu, Teng Li, Ronghu Mao, Chi Du,
and Jianfei Liu
Online Target Volume Estimation and Prediction from an Interlaced
Slice Acquisition - A Manifold Embedding and Learning Approach . . . . . . . 78
John Ginn, James Lamb, and Dan Ruan
One-Dimensional Convolutional Network for Dosimetry Evaluation
at Organs-at-Risk in Esophageal Radiation Treatment Planning. . . . . . . . . . . 86
DashanJiang,TengLi,RonghuMao,ChiDu,YongbinLiu,ShuolinLiu,
and Jianfei Liu
Unpaired Synthetic Image Generation in Radiology Using GANs . . . . . . . . . 94
Denis Prokopenko, Joël Valentin Stadelmann, Heinrich Schulz,
Steffen Renisch, and Dmitry V. Dylov
Deriving Lung Perfusion Directly from CT Image Using Deep
Convolutional Neural Network: A Preliminary Study. . . . . . . . . . . . . . . . . . 102
Ge Ren, Wai Yin Ho, Jing Qin, and Jing Cai
Individualized 3D Dose Distribution Prediction Using Deep Learning . . . . . . 110
JianhuiMa,TiBai,DanNguyen,MichaelFolkerts,XunJia,WeiguoLu,
Linghong Zhou, and Steve Jiang
Deep Generative Model-Driven Multimodal Prostate Segmentation
in Radiotherapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
Kibrom Berihu Girum, Gilles Créhange, Raabid Hussain,
Paul Michael Walker, and Alain Lalande
Dose Distribution Prediction for Optimal Treamtment of Modern
External Beam Radiation Therapy for Nasopharyngeal Carcinoma. . . . . . . . . 128
Bilel Daoud, Ken’ichi Morooka, Shoko Miyauchi, Ryo Kurazume,
Wafa Mnejja, Leila Farhat, and Jamel Daoud
DeepMCDose: A Deep Learning Method for Efficient Monte
Carlo Beamlet Dose Calculation by Predictive Denoising
in MR-Guided Radiotherapy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Ryan Neph, Yangsibo Huang, Youming Yang, and Ke Sheng
UC-GAN for MR to CT Image Synthesis. . . . . . . . . . . . . . . . . . . . . . . . . . 146
Haitao Wu, Xiling Jiang, and Fucang Jia
CBCT-Based Synthetic MRI Generation for CBCT-Guided
Adaptive Radiotherapy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
Yang Lei, Tonghe Wang, Joseph Harms, Yabo Fu, Xue Dong,
Walter J. Curran, Tian Liu, and Xiaofeng Yang