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Taxonomy: IM/TH- Image Segmentation Techniques: Machine Learning
| BReP-SNAP-I-5 | An Empirical Comparison of Weka Classifiers for Outcome Prediction Using An Imaging Habitats Definition and Feature Extraction Method On MRI Q Han1*, R Palm2, K Latifi2, E Moros2, A Naghavi2, G Zhang2, (1) University of South Florida, Tampa, FL, (2) H. Lee Moffitt Cancer Center, Tampa, FL |
| BReP-SNAP-I-10 | Classification of Optical Coherence Tomography Images Using Deep Neural Networks J Kotoku1*, T Tsuji1, Y Hirose1, K Fujimori1, T Hirose1, A Oyama1, Y Saikawa1, T Mimura2, K Shiraishi2, T Kobayashi1, A Mizota2, (1) Graduate School of Medical Care and Technology, Teikyo University, Itabashi-ku,JP, (2) Teikyo University School of Medicine,Itabashi-ku,JP |
| BReP-SNAP-M-3 | A Comparison of Convolutional Neural Networks and Logistic Regression for the Detection of Vertebral Body Misalignments During Radiation Therapy R Petragallo*, J Lamb, University of California, Los Angeles, Los Angeles, CA |
| BReP-SNAP-M-4 | A Comprehensive Framework for Radiotherapy Treatment Plan Quality Evaluation in Large Multiple Institution Dataset L Yuan*, R Kapoor, W Sleeman, M Hagan, J Palta, M Rosu-Bubulac, Virginia Commonwealth University, Richmond, VA |
| BReP-SNAP-M-6 | A Deep Learning Model to Predict a Diagnosis of MCI by Using Static and Dynamic Brain Connectomics D Cui1,2, J Jin2, Z Liu2, T Yin2*, (1) Shandong First Medical University, Taian, 37, CN, (2) Institute Of Biomedical Engineering, Chinese Academy Of Medical Sciences, Tianjin, CN |
| BReP-SNAP-M-7 | A Depthwise Separable Convolution Neural Network for Survival Prediction of Head & Neck Cancer R Li1*, A Das2, N Bice1, P Rad2, A Roy2, N Kirby1, N Papanikolaou1, (1) University of Texas HSC SA, San Antonio, Texas, (2) The University of Texas at San Antonio |
| BReP-SNAP-M-9 | A Method to Predict the Patient-Specific Dose-Volume Histogram Curves for Radiotherapy Planning with Deep Learning X Chen*, K Men, J Yi, J Dai, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, 100021,China |
| BReP-SNAP-M-13 | A Novel Real-Time Markerless Target Tracking Pipeline Based On Faster R-CNN for Lung Cancer Radiotherapy L Deng1,4*, Z Dai2, X Liang3,4, H Zhao4, H Quan1, Y Xie4, (1) Wuhan University, Wuhan, Hubei, CN, (2) The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, CN, (3) Stanford Univ School of Medicine, Stanford, CA, (4) Shenzhen Institute Of Advanced Technology,Shenzhen, Guangdong, CN |
| BReP-SNAP-M-15 | An Adversarial Machine Learning Framework and Biomechanical Model Guided Approach for Generating 3D Lung Tissue Elasticity From Low Dose End-Exhalation CT A Santhanam1, B Stiehl1*, M Lauria1, I Barjaktarevic1, S Hsieh2, D Low1, (1) University of California, Los Angeles, Los Angeles, CA, (2) Mayo Clinic, Rochester, MN |
| BReP-SNAP-M-23 | Automatic Location and Size Selection of Round Applicators for AccuBoost Treatments F West1,2*, S Roles1,2, J Patrick2,3, R Munbodh2,3, M Rivard2,3, J Hepel2,3, K Leonard2,3, D Wazer2,3, Z Saleh2,3, (1) University of Rhode Island, Kingston, RI, (2) Rhode Island Hospital, Providence, RI, (3) Warren Alpert Medical School of Brown University, Providence |
| BReP-SNAP-M-24 | Automatic Target Segmentation and Uncertainty Prediction for Post-Prostatectomy Radiotherapy Planning Using Bayesian U-Net X Xu*, C Lian, A Wang, T Royce, R Chen, J Lian, D Shen, University of North Carolina at Chapel Hill, Chapel Hill, NC |
| BReP-SNAP-M-31 | Can Machine Learning Optimization (MLO) Auto Planning Clinically Replace Multi-Criteria Optimization (MCO) Manual Planning in Complex Pancreatic VMAT with Dose Painting? Y Wang*, H Prichard, J Wo, T Hong, Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA |
| BReP-SNAP-M-39 | Combining Radiomics and Convolutional Neural Network to Predict Tumor Growth of Vestibular Schwannoma K Wang*, L Chen, N George-jones, J Hunter, J Wang, UT Southwestern Medical Center, Dallas, TX |
| BReP-SNAP-M-49 | Deep Learning Augmented Proton Portal Imaging: A Phantom Study S Charyyev1*, Y Lei2, J Harms3, B Eaton4, M McDonald5, W Curran6, T Liu7, J Zhou8, R Zhang9, X Yang10, (1) Emory University, Atlanta, GA, (2) Emory University, Atlanta, GA, (3) Emory University, Atlanta, GA, (4) Emory University, Atlanta, ,(5) Emory University, Atlanta, GA, (6) Emory University, Atlanta, GA, (7) Emory University, Atlanta, GA, (8) Emory University, Atlanta, GA, (9) Dartmouth College, Lebanon, NH, (10) Emory University, Atlanta, GA |
| BReP-SNAP-M-51 | Deep Learning-Based Deformable Image Registration Method and Multiple Reference Registration Strategy for Tumor Target Tracking in 2D Cine MRI y zhang*, T Mazur, X Wu, H Li, H Gach, D Yang, Washington University in St. Louis, St. Louis, MO |
| BReP-SNAP-M-52 | Deep Neural Network for Survival Analysis Using Intratumoral Radiomics and Dosiomics in the RTOG 0617 Clinical Trial S Lee*, H Geng, H Zhong, Y Fan, M Rosen, Y Xiao, University of Pennsylvania, Philadelphia, PA |
| BReP-SNAP-M-55 | Detecting Pathological Complete Response in Esophageal Cancer After Neoadjuvant Therapy Based On Survival-Weighted Deep Learning: A Pilot Study S Cheng1*, W Yap2, E Tu3, (1) Taiwan AI Labs, ,,(2) Chang Gung Memorial Hospital, ,,(3) Taiwan Ai Labs, |
| BReP-SNAP-M-80 | Hard Constraint Approximation for Deep Learning in Radiation Therapy R McBeth*, M Lin, A Godley, S Jiang, D Nguyen, UT Southwestern Medical Center, Dallas, TX |
| BReP-SNAP-M-84 | Image Processing System by Super-Resolution Using Deep Learning Leading to Exposure Dose Reduction H Miyauchi1,2*, Y Tanaka1, K Takahashi1, M Nakano2, T Hasegawa3, M Hashimoto3, (1) Kitasato University Graduate School of Medical Sciences, Sagamihara, Kanagawa, JP, (2) Cancer Institute Hospital of JFCR, Koto-ku, Tokyo, JP, (3) Faculty of Allied Health Sciences, Kitasato university, Sagamihara, Kanagawa, JP |
| BReP-SNAP-M-90 | Incorporating Explicit Dose-Volume Constraints in Deep Learning Improves Prediction of Deliverable Dose Distributions for Prostate VMAT Planning S Willems1*, L Vandewinckele1, E Sterpin2, K Haustermans3, W Crijns3, F Maes1, (1) KU Leuven, BE (2) KU Leuven/UC Louvain, BE (3) KU Leuven/UZ Leuven, BE |
| BReP-SNAP-M-105 | Multi-Strategy Machine Learning Auto Planning for Liver SBRT: Improving Quality, Consistency and Efficiency for a Complex Treatment H Prichard*, J Wo, T Hong, Y Wang, Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA |
| BReP-SNAP-M-106 | Novel Angle Descriptor Projection Overlap Volume for Improved Quality Assurance of Lung Cancer Radiotherapy Treatment Plans J Zhang1*, Y Yang2, Y Chen2, J Zhang3, M Chen2, (1) The Hong Kong Polytechnic University, Hung Hom, Kowloon, HKSAR, (2) Zhejiang Cancer Hospital, University Of Chinese Academy Of Sciences, Hangzhou, Zhejiang, CHN, (3) Duke University Medical Center, Durham, NC |
| BReP-SNAP-M-111 | Patient-Specific Deep Learning Model for Deformable Image Registration s amini*, Z Jiang, Y Chang, Y Mowery, L Ren, Duke University Medical Center, Cary, NC |
| BReP-SNAP-M-120 | Quantum-Inspired Approach to Predicting Geometric Changes in Head and Neck Cancer J Pakela*, R Ten Haken, D McShan, M Matuszak, I El Naqa, University of Michigan, Ann Arbor, MI |
| BReP-SNAP-M-146 | Uncertainty-Aware Reconstructed Image Correction for Proton Computed Tomography Using Bayesian Deep Learning Y Nomura1*, S Tanaka1, J Wang1, H Shirato1, S Shimizu1, L Xing1,2, (1) Hokkaido University, Sapporo, Hokkaido, Japan, (2) Stanford University, Palo Alto, CA |
| BReP-SNAP-T-12 | A Method for Automatic Optimization of Breast Electronic Tissue Compensation Treatment Plans Based On the Breast Radius and Separation A Podgorsak1,2*, L Kumaraswamy3, (1) University at Buffalo, Buffalo, NY, (2) Roswell Park Comprehensive Cancer Center, Buffalo, NY, (3) Novant Health, Winston-Salem, NC |
| BReP-SNAP-T-19 | Accelerated Multi-Criterial Optimization for Pancreas SBRT Using Machine-Learning Dose Prediction P. James Jensen*, Jiahan Zhang, Q. Jackie Wu, Duke University Medical Center, Durham, NC |
| BReP-SNAP-T-28 | Bayesian Stochastic Frontier Analysis with Missing Data Management as Knowledge-Based Planning for Lung SBRT A Kroshko1,2*, O Morin3, L Archambault1,2, (1) Universite Laval, Quebec, QC, CA (2) CHU de Quebec - Hotel-Dieu de Quebec, Quebec, QC, CA (3) University of California San Francisco, San Francisco, CA, USA |
| BReP-SNAP-T-36 | Comparison of Pareto Surface Interpolations for Multi-Criterial Optimization Algorithm Evaluation P. James Jensen1,2*, Jiahan Zhang2, Q. Jackie Wu2, (1) Duke University Medical Center, Durham, NC, (2) Duke University Medical Center, Durham, NC |
| BReP-SNAP-T-41 | Development and Customization of the RayStation Automatic Breast Planning Module: A Program to Adapt the Provided Module to Suit Clinical Needs C Collins*, M Kaluarachchi, E Yu, J Hepel, M Schwer, Brown University Warren Alpert Medical School/Rhode Island Hospital, Providence, RI |
| BReP-SNAP-T-73 | Hierarchical Deep Reinforcement Learning for Intelligent Automatic Radiotherapy Treatment Planning C Shen*, L Chen, Y Gonzalez, D Nguyen, S Jiang, X Jia, The University of Texas Southwestern Medical Ctr, Dallas, TX |
| BReP-SNAP-T-78 | Impact of Enhanced CT-Based Heart Model On Estimating Radiation Therapy Related Late-Onset Cardiac Disease in the Childhood Cancer Survivor Study S Shrestha1, 2*, Q Liu3, J Bates4, Y Yasui5, A Gupta1, 2, C Owens1, 2, S Smith1, R Weathers1, C Lee6, B Hoppe7, W Leisenring8, K Oeffinger9, L Constine10, D Mulrooney5, G Armstrong5, R Howell1, 2, (1) MD Anderson Cancer Center, TX (2) UTHealth Graduate School of Biomedical Sciences, TX (3) University Of Alberta, Canada (4) University Of Florida, FL (5) St. Jude Children's Research Hospital, TN (6) National Cancer Institute, MD (7) Mayo Clinic, FL (8) Fred Hutchinson Cancer Research Center, WA (9) Duke University, NC (10) University of Rochester Medical Center, NY |
| BReP-SNAP-T-84 | Improving Plan Quality Through Automation of Treatment Planning Processes Using Scripting in RayStation G Jarry*, M Ayles, M Brunet-Benkhoucha, D Martin, Hopital Maisonneuve-Rosemont, Montreal, CA, |
| BReP-SNAP-T-107 | Multivariable Dosimetric Models for Urinary and Rectal Toxicity Prediction Assessed From Patient Reported Outcome After Prostate Stereotactic Body Radiotherapy X Pan1*, J Huang2, R Levin-epstein3, Z Wang4, D Low5, X Qi6, (1) Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing,, Xi'an, ,CN, (2) ,Xi'an, 61, CN, (3) ,,,(4) ,,,(5) UCLA, Los Angeles, CA, (6) UCLA School of Medicine, Los Angeles, CA |
| BReP-SNAP-T-108 | ORBIT-RT: A Real-Time, Open Platform for Knowledge-Based Quality Control of Radiotherapy Treatment Planning B Covele*, K Puri, K Kallis, K Moore, UC San Diego, La Jolla, CA |
| BReP-SNAP-T-114 | Predicting Optimal Plan Dose Distributions of Total Marrow Irradiation Using Deep Learning Model D Du*, J Neylon, J Wong, A Liu, City of Hope Medical Center, Duarte, CA |
| BReP-SNAP-T-132 | Towards a Treatment Planning Optimization Framework Utilizing Predicted Quality Assurance Outcomes From a Machine Learning Model to Maximize Plan Quality and Deliverability P Wall1*, J Fontenot1,2, (1) Louisiana State University, Baton Rouge, LA, (2) Mary Bird Perkins Cancer Center, Baton Rouge, LA |
| BReP-SNAP-T-138 | Using Deep Learning to Predict Beam-Tunable Pareto Optimal Dose Distribution for Intensity Modulated Radiation Therapy G Bohara*, A Sadeghnejad Barkousaraie, S Jiang, D Nguyen, UT Southwestern Medical Center, Dallas, TX |
| BReP-SNAP-T-141 | What Knowledge-Based Dose Prediction Models Tell Us About Ovoid Vs. Ring Based Brachytherapy Applicators K Kallis*, B Covele, A Simon, D Brown, D Scanderbeg, K Kisling, C Yashar, J Einck, L Mell, J Mayadev, K Moore, S Meyers, UC San Diego, La Jolla, CA |
| MO-CD-TRACK 1-1 | An Artificial Intelligence-Driven Agent for Rapid Head-And-Neck IMRT Plan Generation Using Conditional Generative Adversarial Networks (cGAN) X Li1*, Y Sheng1, J Zhang1, W Wang1, F Yin1, Q Wu1, Y Ge2, Q Wu1, C Wang1, (1) Duke University Medical Center, Durham, NC, (2) University of North Carolina at Charlotte, Charlotte, NC |
| MO-EF-TRACK 3-15 | Deep DoseNet: A Deep Neural Network Based Dose Calculation Algorithm P Dong1*, L Xing1, (1) Stanford Univ School of Medicine, Stanford, CA |
| MO-F-TRACK 2-2 | A Deep Learning Based Segmentation and Evaluation Framework for Brain Metastases Follow-Up After Stereotactic Radiosurgery Z Yang1*, L Wang2, Y Liu3, M Chen1, E Zhang1, R Timmerman1, T Dan1, Z Wardak1, W Lu1, X Gu1, (1) UT Southwestern Medical Center, Dallas, TX (2) University Of Texas At Arlington, TX (3) Sichuan University, Chengdu, CN |
| MO-F-TRACK 2-4 | OARnet: Organs-At-Risk Delineation in Head and Neck CT Images M H Soomro*, H Nourzadeh, V Leandro Alves, W Choi, J Siebers, University of Virginia, Charlottesville, VA |
| PO-GeP-I-5 | A Deep Learning Approach On Cirrhosis Diagnosis Utilizing Ultrasound B-Mode Images of Segmented Liver Left Lobes Using Liver Biopsy as the Gold Standard P Drazinos1, I Gatos2, S Tsantis2, 3, P Zoumpoulis1, I Theotokas1, D Mihailidis4, G Kagadis2*, (1) Diagnostic Echotomography S.A.,Athens, GR, (2) University of Patras, Rion, GR, (3) University of West Attica, Athens, GR, (4) University of Pennsylvania, Philadelphia, PA, USA |
| PO-GeP-I-6 | A Deep Learning-Based End-To-End CT Reconstruction Method K Lu*, L Ren, F Yin, Duke University, Durham, NC |
| PO-GeP-I-73 | Deep-Learning-Based CT ImageStandardization to Improve Stability of Radiomics Features in Non-Small CellLung Cancer J Zhang1*, M Selim2, M Brooks3, B Fei4, G Zhang5, J Chen6, (1) University of Kentucky, Lexington, KY, (2) University Of Kentucky, ,,(3) University Of Kentucky, ,,(4) University of Texas (UT) at Dallas and UT Southwestern Medical Center, Richardson, TX, (5) Uthealth, ,,(6) University Of Kentucky, |
| PO-GeP-I-107 | Estimation of X-Ray Energy Spectrum for CT Scanner From Percentage Depth Dose Measurement Y Hasegawa1*, A Haga1, D Sakata2, Y Kanazawa1, M Tominaga1, M Sasaki1, T Imae3, K Nakagawa3, (1) University of Tokushima, Tokushima, JP, (2) National Institute of Radiological Sciences, Chiba, JP, (3) The University of Tokyo Hospital, Tokyo, JP |
| PO-GeP-I-119 | Evaluation of Synthetic CT Generation From CBCT Using a Deep Learning Model A Haidari1,2*, D Granville2, E Ali1,2, (1) Carleton University, Ottawa, ON, CA, (2) The Ottawa Hospital Cancer Centre, Ottawa, ON, CA |
| PO-GeP-I-127 | Gray Matter-Based Radiomics and Machine Learning for the Diagnosis of Attention-Ddeficit/Hyperactivity Disorder S Zhao1*, Z Mu2, H Zhao3, J Qiu3, W Lu3, W Lu3, L Shi3, (1) Beijing Anding Hospital, Capital Medical University, Beijing, CN, (2) The Second Affiliated Hospital Of Shandong First Medical University, Taian, CN, (3) Shandong First Medical University & Shandong Academy Of Medical Sciences, Taian, CN |
| PO-GeP-I-170 | Predicting the Severity of White Matter Hyperintensities Using Structural MRI and Machine Learning W Lu1*, H Li2, L Zheng1, L Shi1, W Lu1, J Qiu1, (1) Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, Shandong, CN, (2) Shandong University Of Science And Technilogy, Qingdao, Shandong, CN |
| PO-GeP-I-190 | Shape Analysis in PET Images Using Convolutional Neural Nets: Limitations of Standard Architectures I Klyuzhin1,2*, A Rahmim1,2, (1) BC Cancer Research Centre, Vancouver, BC, CA, (2) University of British Columbia, Vancouver, BC, CA |
| PO-GeP-I-219 | Training and Validation of a Commercial Deep Learning Contouring Platforms J Koo*, J Caudell, V Feygelman, E Moros, K Latifi, H. Lee Moffitt Cancer Center, Tampa, FL |
| PO-GeP-M-9 | A Deep Learning Method for Prediction of 3D Dose Distribution in Clinical Case S Ahn1*, E Kim2, K Kim3, C Kim4, S Lee5, Y Lim6, H Kim7, D Shin8, J Jeong9, (1) National Cancer Center, Goyang-si Gyeonggi-do,KR |
| PO-GeP-M-14 | A Feasibility Study On the Development of a Deep Learning-Based Whole Brain Irradiation Automated Dose Calculation Algorithm A Jaffe*, J Keller, Thomas Jefferson University, Philadelphia, PA |
| PO-GeP-M-17 | A Hierarchical 3D U-Net for Brain Tumor Substructure Segmentation J Yang1, R Wang1,2,3, Y Weng2,3*, L Chen2,3, Z Zhou4, (1) School of Artificial Intelligence, Xidian University, Xi'an, CN. (2) UT Southwestern Medical Center, Dallas, TX. (3) Medical Artificial Intelligence and Automation (MAIA) Lab, University of Texas Southwestern Medical Center, Dallas, TX. (4) University Of Central Missouri, Warrensburg, MO. |
| PO-GeP-M-30 | A Novel Semi-Supervised Learning Method Using Soft-Label for Lung Segmentation On CT J Zhou1*, Z Yan2, Y Zhang1, N Yue1, (1) Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, (2) SenseBrain Technology Limited LLC, Princeton, NJ |
| PO-GeP-M-78 | Automated Identification of DICOM-RT Structures Using Map Projections and Machine Learning D Cutright1*, T Wu1, A Roy2, M Gopalakrishnan3, B Mittal3 (1) University of Chicago Medicine, Chicago, IL, (2) The University of Texas at San Antonio, San Antonio, TX, (3) Northwestern Memorial Hospital, Chicago, IL |
| PO-GeP-M-79 | Automatic Prostate Bed Target Segmentation On Daily Cone-Beam CT Image Using a Multi-Path 3D Dense-UNet J Fu1*, S Yoon1, A Kishan1, K Singhrao1, Z Wang1, J Lewis2, D Ruan1, (1) Department Of Radiation Oncology, UCLA, Los Angeles, CA, (2) Cedars-Sinai Medical Center, Beverly Hills, CA. |
| PO-GeP-M-84 | Auto-Segmentation On Liver With U-Net And Pixel Deconvolutional U-Net H Yao*, J Chang, Northwell Health and Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Lake Success, NY |
| PO-GeP-M-99 | Clinical Evaluation of Deep Learning and Atlas Based Auto-Contouring of Bladder and Rectum for Prostate Radiotherapy J Zabel1,2, J Conway2,3, A Gladwish2,3, J Skliarenko2,3, G Didiodato1,2, L Goorts-matthews2, A Michalak2, S Reistetter2, J King2, K Malkoske2, K Nakonechny2, M Tran2, N McVicar2*, (1) McMaster University, Hamilton, ON, Canada, (2) Simcoe Muskoka Regional Cancer Program, Barrie, ON, Canada, (3) Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada |
| PO-GeP-M-110 | Comparison of Artificial Intelligence Based Decision Support Tool for Head and Neck Treatment Planning R McBeth*, D Nguyen, H Wang, R Norouzi-Kandalan, m mashayekhi, X Zhong, Z Iqbal, A Godley, S Jiang, M Lin, UT Southwestern Medical Center, Dallas, TX |
| PO-GeP-M-119 | Compton Camera Event Classification Using Artificial Neural Networks P Maggi1*, C Barajas2, G Kroiz2, J Basalyga2, S Peterson3, D Mackin4, R Panthi4, S Beddar4, M Gobbert2, J Polf1, (1) University of Maryland, Baltimore, Baltimore, MD, (2) University Of Maryland, Baltimore County, Baltimore, MD, (3) University of Cape Town, Rondebosch, ZA, (4) The University of Texas M.D. Anderson Cancer Center, Houston, TX |
| PO-GeP-M-123 | Convolutional Neural Network Learning From RT Dose Distribution and Images Improves Predicting Locoregional Recurrence for Head and Neck Cancer A Wu1*, Y Li2, M Qi1, X Lu1, Y Liu1, L Zhou1, T Song1, (1) Southern Medical University, Guangzhou, Guangdong, CN, (2) Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, CN |
| PO-GeP-M-134 | Deep Learning Based Treatment Plan Evaluation X Zhong*, D Nguyen, R McBeth, A Balagopal, m mashayekhi, M Lin, S Jiang, UT Southwestern Medical Center, Dallas, TX |
| PO-GeP-M-136 | Deep Learning Prediction of Radiotherapy Treatment Machine Parameters L Hibbard1*, (1) Elekta, Inc, St. Charles, MO |
| PO-GeP-M-137 | Deep Learning Segmentation of Cardiac Substructures in Breast Cancer Radiotherapy Patients X Jin*, J Hilliard, J Dise, J Kavanaugh, I Zoberi, M Thomas, C Robinson, G Hugo, Washington University School of Medicine, St. Louis, MO |
| PO-GeP-M-138 | Deep Learning-Based Auto-Segmentation of OARs in Head and Neck CT Images Z Shen1*, A Garsa1, S Sun2, N Bai2, C Zhang2, A Shiu1, E Chang1, W Yang1, (1) University of Southern California, Los Angeles, CA, (2) DeepVoxel Inc., Irvine, CA |
| PO-GeP-M-139 | Deep Proton DoseNet: A Deep Neural Network for Proton Dose Distribution Image Super-Resolution Y Nomura1*, T Matsuura1, H Shirato1, S Shimizu1, L Xing1,2, (1) Hokkaido University, Sapporo, Hokkaido, Japan, (2) Stanford University, Palo Alto, CA |
| PO-GeP-M-165 | Dose Prediction and Customized Optimization Settings by Learning From Previous Cases: Application to SBRT Treatment Planning E Schreibmann*, Department of Radiation Oncology and Winship Cancer Institute of Emory University |
| PO-GeP-M-168 | Dosimetric Analysis of Automated Treatment Planning for Whole Brain Radiotherapy with A Deep Learning Approach E Han*, C Cardenas, C Nguyen, T Briere, j Li, D Yeboa, C Wang, L Court, M Martel, Z Wen, University of Texas MD Anderson Cancer Center, Houston, TX |
| PO-GeP-M-169 | Dosimetric Analysis of OARnet Auto-Delineations for Head and Neck Organs-At-Risk M H Soomro*, H Nourzadeh, V Leandro Alves, W Choi, J Siebers, University of Virginia, Charlottesville, VA |
| PO-GeP-M-197 | Evaluation of a Novel Automated Treatment Planning Tool for Cervical Cancer in IMRT J Guo*, J Zhou, L Chen, J Ni, Y Xu, G Gan, W Gon, C Ma, Y Li, W Zhan, X Xu, S Qin, The first affiliated hospital of Soochow University, Suzhou, JiangsuCN, |
| PO-GeP-M-202 | Evaluation of Machine Learning Algorithms for Treatment Planning Parameter Calculation J Chow1*, R Jiang2, F Ng3, (1) Princess Margaret Cancer Centre, Toronto, ON, CA, (2) Grand River Hospital, Kitchener, ON, CA, (3) Ryerson University, Toronto, ON, CA |
| PO-GeP-M-232 | Generating Synthetic CT From Daily Cone Beam CT Using Machine Learning Models SA Yoganathan, S Paloor*, R Hammoud, N Hammadi, National Centre for Cancer Care & Research, HMC, Doha, QATAR. |
| PO-GeP-M-235 | Head Neck Cancer Locoregional Recurrence Prediction Using Delta-Radiomics Feature K Wang1*, Z Zhou2, L Chen3, R Wang4, D Sher5, J Wang6, (1) UT Southwestern Medical Center, Dallas, TX, (2) University Of Central Missouri, Warrensburg, MO, (3) UT Southwestern Medical Center, Dallas, TX, (4) UT Southwestern Medical Center, Dallas, TX, (5) UT Southwestern Medical Center, Dallas, TX, (6) UT Southwestern Medical Center, Dallas, TX |
| PO-GeP-M-241 | Impact of Deep Learning Based Image Quality Augmentation On CBCT Based Radiomics Analysis M Huang*, Z Zhang, J Lee, Z Jiang, T Niu, F Yin, L Ren, Duke University Medical Center, Cary, NC |
| PO-GeP-M-252 | Improved Auto-Segmentation for CT Male Pelvis: Comparison of Deep Learning to Traditional Atlas Segmentation Methods C Halley*, H Wan, A Kruzer, D Pittock, D Darkow, M Butler, N Cole, M Bending, P Jacobs, AS Nelson, MIM Software Inc., Cleveland, OH |
| PO-GeP-M-257 | Independent Feature Selection in Radiomics Cross-Validation Is Essential to Estimate the True Model Performance J Du*, X Qi, R Chin, K Sheng, UCLA School of Medicine, Los Angeles, CA |
| PO-GeP-M-261 | Initial Experience in MRI-Based Brain Metastases Detection Using Deep Learning J Teruel1*, K Bernstein1, P Galavis1, K Spuhler1, J Silverman1, D Kondziolka1,2, K Osterman1, (1) Department of Radiation Oncology, NYU Langone Health, New York, NY, (2) Center for Advanced Radiosurgery, NYU Langone Health, New York, NY |
| PO-GeP-M-263 | Interactive Contouring Through Contextual Deep Learning M Trimpl1,2,3*, D Boukerroui1, E Stride2, K Vallis3, M Gooding1, (1) Mirada Medical Ltd, New Barclay House, 234 Botley Rd, Oxford OX2 0HP, GB (2) Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus OX37DQ, GB (3) Oxford Institute for Radiation Oncology, University of Oxford, Old Road Campus OX37DQ, GB |
| PO-GeP-M-280 | Learned Delineation of Gross Tumor Volume Incorporating Intra-Observer Variability T Marin*, C Ma, R Lahoud, F Xing, P Wohlfahrt, M Moteabbed, J Woo, X Ma, K Grogg, Y Chen, G El Fakhri, Massachusetts General Hospital, Boston, MA |
| PO-GeP-M-286 | Longitudinal Analysis of Parotid Gland Anatomical Changes During Radiotherapy by Recurrent Convolutional Neural Networks D Lee*, P Zhang, S Alam, J Jiang, S Nadeem, A Caringi, N Allgood, Y Hu, Memorial Sloan Kettering Cancer Center, New York, NY |
| PO-GeP-M-304 | Multi-Modality Brain Tumor Segmentation Using a Modified Cascaded 3D U-Net for Imbalanced Classes W Kong1*, D Li1, YM Yang2, (1) Shandong Normal University, Jinan, 37, CN, (2) UCLA, Los Angeles, CA |
| PO-GeP-M-310 | Normal Tissue and Tumor Segmentation Using V-Net Regularized by YOLO C Hsu1*, C Morin1, T Kirby2, M Metzger1, J Flerlage1, S Kaste1, M Krasin1, B Shulkin1, J Lucas1, (1) St. Jude Children's Research Hospital, Memphis, TN, (2) Rhodes College, Memphis , TN |
| PO-GeP-M-319 | Optimizing Treatment Plan Selection of HPV-Associated OPSCC Patients Using Artificial Intelligence On Electronic Medical Records and Radiomics T Bejarano*, I Mihaylov, M Samuels, University of Miami, Miami, FL |
| PO-GeP-M-320 | Organ Segmentation From CT Images Using Super Perception Convolutional Neural Networks for Cervical Cancer Brachytherapy Z Zhang1*, S Wang1, Y He2, R Zhou1, Z Jin1, P Xie2, J Wei2, (1) Xiangya Hospital Central South of University, Changsha, Hunan,CN, (2) Perception Vision Medical Technology, Guangzhou, Guangdong,CN, |
| PO-GeP-M-333 | Prediction of Optimal Weighting Factors Into the Objective Function On IMRT Plans E Cisternas Jimenez*, F Yin, Duke University Medical Center, Durham, NC |
| PO-GeP-M-338 | Prognostic Value of Imaging-Based Estimates of Glioma Pathology Pre- and Post-Surgery E Gates*, D Suki, J Weinberg, S Prabhu, D Fuentes, D Schellingerhout, UT MD Anderson Cancer Center, Houston, TX |
| PO-GeP-M-347 | Quantification of Intrafraction Prostate Motion Using Detected Features in Sagittal 2D Cine-MR B Strbac*, C Brouwer, S Both, J Langendijk, D Yakar, S Al-uwini, |
| PO-GeP-M-355 | Race-Telltales From Blinded Notes? Clinical Evidence Or Implicit Bias H Zhou1, D Ruan12*, (1) Dept. of Bioengineering, UCLA, Los Angeles, CA, (2) Dept. of Radiation Oncology, UCLA, Los Angeles, CA |
| PO-GeP-M-360 | Real-Time Long Range Respiratory Prediction J Prinable*, R O'Brien, ACRF Image X Institute, University of Sydney Central Clinical School, Sydney, AU |
| PO-GeP-M-366 | Reducing IMRT QA Workload by 95% and Keeping the Same Level of Quality Control T Nano1*, M Descovich1, E Hirata1, Y Interian2, G Valdes1, (1) University of California, San Franisco, San Francisco, CA, (2) USF, San Francisco, CA |
| PO-GeP-M-367 | Region Specific Dose Prediction Using Deep Neural Networks: A Feasibility Study On the Planning Target Volume of Prostate IMRT Patients D Nguyen*, S Jiang, Medical Artificial Intelligence and Automation (MAIA)Laboratory, UT Southwestern Medical Center, Dallas, TX |
| PO-GeP-M-370 | Robustness Study of Deep Learning Based Medical Image Segmentation to Noisy Annotation S Yu*, E Zhang, J Wu, H YU, L Ma, Z Yang, M Chen, X Gu, W Lu, UT Southwestern Medical Center, Dallas, TX |
| PO-GeP-M-388 | Study On Intelligent Treatment Planning for Left Breast Cancer X He*1, B Su2, R Zhao2, (1)Ruijin Hospital and Shanghai Jiaotong University School of Medicine,Shanghai,CN (2) Shanghai Pulmonary Hospital and Tongji University School of Medicine,Shanghai,CN |
| PO-GeP-M-390 | Synthetic Contrast Enhancement of Cone Beam Computed Tomography (CBCT) for Adaptive Radiotherapy O Dona*, Y Wang, D Horowitz, A Xu, J Rickman, C Wuu, Columbia Univ, New York, NY |
| PO-GeP-M-408 | Towards Safer Artificial Intelligence-Based Radiation Therapy Treatment Planning: Adding Uncertainty Estimation to Volumetric Dose Prediction Using An Approximate Bayesian Method On Deep Neural Networks D Nguyen*, A Balagopal, A Sadeghnejad Barkousaraie, R McBeth, S Jiang, Medical Artificial Intelligence and Automation (MAIA) Laboratory, UT Southwestern Medical Center, Dallas, TX |
| PO-GeP-M-409 | Transferring Beam Navigation Behavior From Human to Robot: An Evidence Driven Decision Making Model for Liver SBRT Y Sheng*, W Wang, R Li, C Wang, J Zhang, X Li, H Stephens, Q Wu, F Yin, Y Ge, Q Wu, Duke University Medical Center, Durham, NC |
| PO-GeP-M-412 | Unboxing Artificial Intelligence "black-Box" Models - A Novel Heuristic S Weppler1,2*, H Quon1,2, N Harjai1, C Beers1, L Van Dyke2, C Kirkby1,2,3, C Schinkel1,2, W Smith1,2, (1) University of Calgary, Calgary, AB, CA, (2) Tom Baker Cancer Centre, Calgary, AB, CA, (3) Jack Ady Cancer Centre, Lethbridge, AB, CA. |
| PO-GeP-M-416 | Using Raman Spectroscopy and Machine Learning to Predict and Monitor Cellular Radiation Responses X Deng*, K Milligan, R Ali-Adeeb, P Shreeves, S Van Nest, J Andrews, A Brolo, J Lum, A Jirasek, University of British Columbia, Kelowna, BC, CA, University of Victoria, Victoria, BC, CA, Deeley Research Centre, BC Cancer, Victoria, BC, CA, Weill Cornell Medicine, New York, NY, USA |
| PO-GeP-M-417 | Using Very Small Contour Sets to Train High-Quality Deep-Learning Segmentation Models Y Zhao*, D Rhee, C Cardenas, L Court, J Yang, The University of Texas MD Anderson Cancer Center, Houston, TX |
| PO-GeP-M-418 | Utilizing Knowledge-Based Planning Model to Predict Achievable Prescription Dose for Mesothelioma Patients with Two Intact Lungs L KUO*, A Rimner, E Yorke, Memorial Sloan-Kettering Cancer Center, New York, NY |
| PO-GeP-M-439 | Comparison of a 3D Convolutional Neural Network Segmentation Method to Traditional Atlas Segmentation for CT Head and Neck Contours A Kruzer*, H Wan, M Bending, C Halley, D Darkow, D Pittock, N Cole, P Jacobs, AS Nelson, MIM Software Inc., Cleveland, OH |
| PO-GeP-T-10 | A Comparison of Prediction Models in Autocorrelated Processes for Quality Assurance j Lah1*, G Kim2, D Shin3, (1) Myongji Hospital, Hanyang University College of Medicine, Goyang-si, KR, (2) University of California, San Diego, La Jolla, CA,(3) National Cancer Center, Goyang-si, KR |
| PO-GeP-T-70 | A Study of Reoxygenation of Cancer Cells After Application of Radiation of Different Sized Doses Using a Diffusion Model Y Watanabe, E Dahlman*, University of Minnesota, Minneapolis, MN |
| PO-GeP-T-106 | Analysis of a Machine Learning Based Planning Tool for Parotid Dose Prediction and Sparing N Shaheen*, R Bayliss, P Hill, University of Wisconsin, Madison, WI |
| PO-GeP-T-122 | Assessing the Importance of Oral Cavity Dosimetry On Patient Reported Xerostomia and Dysgeusia in Patients Receiving De-Intensified Treatment for Oropharynx Cancer D Fried*, A Fuquay, S Das, B Chera, C Shen, K Pearlstein, Univ North Carolina, Chapel Hill, NC |
| PO-GeP-T-125 | Assessment of a Knowledge-Based RapidPlan Model for Craniospinal Irradiation (CSI) G Luo*, G Twork, M Price, G Ding, Vanderbilt University, Nashville, TN |
| PO-GeP-T-131 | Atlas-Based PTV Contouring for Total Marrow Irradiation A Cherpak1,2,3*, K Chytyk-Praznik1,2,3, L Mulroy1,2, (1) Nova Scotia Health Authority, Halifax, NS, CA (2) Department Of Radiation Oncology, Dalhousie University, Halifax, NS, CA (3) Department of Physics and Atmospheric Sciences, Dalhousie University, Halifax, NS, CA |
| PO-GeP-T-141 | Automatic IMRT Planning Via Static Field Fluence Prediction (AIP-SFFP): A Novel Local Attention Deep-Learning Design for Head-And-Neck IMRT Application X Li1, J Zhang1, Y Sheng1, Y Chang1, H Stephens1, Q Wu1, F Yin1, Y Ge2, Q Wu1, C Wang1*, (1) Duke University Medical Center, Durham, NC, (2) University of North Carolina at Charlotte, Charlotte, NC |
| PO-GeP-T-144 | Automation of Eyeplaque Procedures for Greater Integration and Safety Q Diot*, D Westerly, M Miften, University of Colorado School of Medicine, Aurora, CO |
| PO-GeP-T-145 | Automation of the Treatment Planning Process for IMRT Prostate Cases P Kapoor1,2*, (1) Virginia Commonwealth University, (2)Hunter Holmes McGuire Veterans Affairs Medical Center, Richmond, VA |
| PO-GeP-T-153 | Benchmarking DVH Prediction Uncertainty Between Different Knowledge-Based Models B Covele*, K Moore, UC San Diego, La Jolla, CA |
| PO-GeP-T-164 | Can Knowledge-Based Dose Prediction Models Inform Brachytherapy Needle Decision-Making for Cervical Cancer? K Kallis*, D Brown, D Scanderbeg, K Kisling, B Covele, A Simon, C Yashar, J Einck, L Mell, J Mayadev, K Moore, S Meyers, UC San Diego, La Jolla, CA |
| PO-GeP-T-183 | CivaSheet Commissioning and Workflow Planning S Morelli1*, J Woollard1, A Ayan1, N Gupta1, (1) The Ohio State University, Columbus, OH |
| PO-GeP-T-236 | Compounding Automation Features From Multiple APIs for Automated Linear Accelerator Quality Assurance M Schmidt1,2*, C Raman1, Y Wu1, N Knutson1, F Reynoso1, Y Hao1, M Yaqoub1, G Hugo1, E Sajo2, B Sun1, (1) Washington University School of Medicine in St. Louis, Saint Louis, MO, AF,(2) Univ Massachusetts Lowell, Lowell, MA |
| PO-GeP-T-391 | Evaluating Knowledge-Based Planning Performance for Challenging Cases: Prostate Bed VMAT with Nodal Involvement and Significant OAR Overlaps L Fu*, Y Lo, Mount Sinai Health System, New York, NY |
| PO-GeP-T-397 | Evaluation of a Novel AI-Driven Automated Treatment Planning System X Ray*, M Moazzezi, C Bojechko, K Moore, University of California San Diego, La Jolla, CA |
| PO-GeP-T-398 | Evaluation of a Novel Automation Software for Generating Field-In-Field Plans for Various Treatment Sites C Esquivel1*, L Patton1, B Doozan2, K Nelson1, D Boga1, T Navarro3, (1) Texas Oncology San Antonio, San Antonio, TX, (2) Texas Oncology, McAllen, TX, (3) Texas Oncology Brownsville,TX |
| PO-GeP-T-509 | Improving Clinical Efficiency and Consistency with RapidPlan for Single Lesion Brain SRS Q Shang*, K Koch, D Wiant, T Hayes, N Koch, B Sintay, H Liu, Cone Health Cancer Center, Greensboro, NC |
| PO-GeP-T-515 | Incorporating Parotid Gland Sub-Region Importance Data Into the Radiotherapy Treatment Planning Process C Sample1,2, J Wu2, S Thomas2, H Clark2, (1) University of British Columbia, Vancouver, BC, CA, (2) BC Cancer Agency, Vancouver, BC, CA |
| PO-GeP-T-522 | Integrating Knowledge-Based Models for An Enhanced Iterative Automated Treatment Planning Process M Vaccarelli1,2*, J Baker3, (1) Hofstra University, Hempstead, NY, (2) Northwell Health, Lake Success, NY, (3) Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY |
| PO-GeP-T-538 | Investigation of Plan Quality Metrics for Multiple-Metastases Using Single-Isocenter Radiosurgery Technique with Millennium MLC E Flores-Martinez1*, G Kim2, (1) University of Chicago Hospitals, Chicago, IL, (2) University of California, San Diego, La Jolla, CA |
| PO-GeP-T-556 | Knowledge-Based RapidPlan Models for Left and Right Breast Using RapidArc O Apaza*, A Garcia, M Almada, C Venencia, Instituto Zunino - Fundacion Marie Curie, Cordoba, ARGENTINA |
| PO-GeP-T-641 | Plan Quality Assessment for Rectal Cancer Patients Using Prediction of Organ-At-Risk Dose Metrics A Vaniqui*, R Canters, F Vaassen, C Hazelaar, I Lubken, K Kremer, C Wolfs, W Van Elmpt, Maastro, Maastricht, NL, |
| PO-GeP-T-652 | Predicting Linac Failure Risks From Machine Performance Check Application Using An Integration of Statistical Process Control and Machine Learning T Fuangrod1*, W Puyati2, A Khawne2, M Barnes3, P Greer3, (1) Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhron Royal Academy, Bangkok,TH (2) Department of Computer Engineering, Faculty of Engineering, King Mongkuts Institute of Technology Ladkrabang, Bangkok, TH (3) Department of Radiation Oncology, Calvary Mater Hospital Newcastle, Newcastle, NSW, AUS |
| PO-GeP-T-654 | Prediction of Three-Dimensional Radiotherapy Optimal Dose Distributions for Lung Cancer Patients with Asymmetric Network Y Shao*, H Chen, Z Xu, Department Of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University |
| PO-GeP-T-707 | Retrospective Dosimetric Study of a Novel Automation Software for Whole Brain Planning Field-In-Field Treatment Plans C Esquivel*, A Tipton, L Patton, D Baldassari, B Lin, Texas Oncology San Antonio, San Antonio, TX |
| PO-GeP-T-775 | The Impact of Knowledge-Based Planning and Multicriteria Optimization On Treatment Planning Workflows J Rogers*, M DiMascio, J Stanulus, W Ramer, A Boe, Atrium Health - Rock Hill and Lancaster Radiation Therapy, Indian Land, SC |
| PO-GeP-T-787 | The Research of Automatic Treatment Planning by DVH Prediction Based On Classifying the OARs Overlap X Pan1,2*, D Wang1,3,4, J Jia1,3,4, L Hu1, FDS Team (1) Institute Of Nuclear Energy Safety Technology, Chinese Academy Of Sciences, CN (2) University of Science and Technology of China, Hefei Anhui, CN, (3) Anhui Engineering Technology Research Center of Accurate Radiotherapy, Hefei Anhui, CN (4) CAS SuperAccuracy Science & Technology Co., Ltd., Nanjing, Jiangsu, CN |
| PO-GeP-T-800 | Towards An Image-Informed Mathematical Model of Response to Fractionated Radiation Therapy D Hormuth,II1,5*, A Jarrett1,5, T Yankeelov1-5, (1) Oden Institute for Computational Engineering and Sciences, Departments of (2) Biomedical Engineering, (3) Diagnostic Medicine, and (4) Oncology, (5) Livestrong Cancer Institutes . The University of Texas at Austin, Austin, TX USA |
| PO-GeP-T-801 | Towards Automation of Treatment Planning System Quality Control J A Lovis1*, L Van Dyke1, M Roumeliotis1,2, K Thind1,2, S Quirk1,2, (1) Tom Baker Cancer Centre, Calgary, AB, CA, (2) University of Calgary, Calgary, AB, CA |
| PO-GeP-T-858 | Automated SBRT Planning Using Constrained Hierarchical Optimization: Three Year Clinical Experience with Over 1900 Patients L Hong*, Y Zhou, Q Huang, J Yang, H Pham, J Mechalakos, M Hunt, J Deasy, M Zarepisheh, Memorial Sloan Kettering Cancer Center, New York, NY |
| SU-CD-TRACK 2-4 | A Data Driven Fully Automated Contouring and Planning Solution for Cervical Cancer D Rhee1*, A Jhingran1, B Rigaud1, K Huang1, K Kisling2, B Beadle3, C Cardenas1, S Kry1, S Vedam1, L Zhang1, K Brock1, W Shaw4, D O'Reilly4, H Simonds5, L Court1, (1) University of Texas MD Anderson Cancer Center, Houston, TX, (2) UC San Diego, La Jolla, CA, (3) Stanford University, Stanford, CA, (4) University of the Free State, Bloemfontein, ,ZA, (5) Stellenbosch University, Stellenbosch, ,ZA, |
| SU-CD-TRACK 2-10 | Deriving Ventilation Imaging From Free Breathing Proton MRI Via Deep Convolutional Neural Network D Capaldi1*, F Guo2, L Xing3, G Parraga4, (1) Stanford University, Stanford, CA, (2) Sunnybrook Research Institute, (3) Stanford Univ School of Medicine, Stanford, CA, (4) Western University, London, ON, CA |
| SU-D-TRACK 1-1 | One to Many Modality Unsupervised Domain Adaptation for Multiple MRI Sequence Abdomen Organ Segmentation in MR-Guided Radiotherapy J Jiang1*, H Veeraraghavan2, (1) MSKCC, New York, NY, (2) Memorial Sloan Kettering Cancer Center, New York, NY |
| SU-E-TRACK 1-2 | Phantom-Based Training Framework for Deep Convolutional Neural Network CT Noise Reduction N Huber*, A Missert, H Gong, S Leng, L Yu, C McCollough, Mayo Clinic, Rochester, MN |
| SU-E-TRACK 1-3 | Machine Learning and Texture Analysis of Thoracic X-Ray Computed Tomography to Reveal Subclinical Emphysema M Sharma1,2*, AR Westcott1,2, JL MacNeil1,3, DG McCormack4 and G Parraga1-4, (1) Robarts Research Institute, (2) Department of Medical Biophysics, (3) School of Biomedical Engineering, (4) Department of Medicine, Western University, London, ON, Canada |
| SU-E-TRACK 3-2 | Error Reduction by Synergizing Event Reporting and Automation M Lin*, Y Park, A Pompos, R Reynolds, A Owrangi, D Sher, S Jiang, A Godley, UT Southwestern Medical Center, Dallas, TX |
| SU-F-TRACK 2-5 | Progressively Grown GAN with Learned Fusion Operation for Hetero-Modal Synthesis of MRI Sequences D Gourdeau1*, S Duchesne2, L Archambault3, (1) Universite Laval, Quebec, QC, CA, (2) Centre de recherche CERVO, Quebec, QC, CA (3) CHUQ Pavillon Hotel-Dieu de Quebec, Quebec, QC, CA |
| TH-A-TRACK 3-2 | Accurate Post-Implantation Dose Reconstruction of CivaSheet, Aided by Machine Learning Techniques I Veltchev*, R Price, X Chen, K Howell, J Meyer, C Ma, Fox Chase Cancer Center, Philadelphia, PA |
| TH-AB-TRACK 4-7 | Deep Learning-Based Auto-Segmentation of Swallowing and Chewing Structures A Iyer*, M Thor, R Haq, J Deasy, A Apte, Memorial Sloan Kettering Cancer Center, New York, NY |
| TH-AB-TRACK 4-8 | Dosimetric Validation of An Artificial Intelligence Encapsulated Knowledge Transfer Across Medical Domains Applied to Heart Contouring for Treatment Planning C V Guthier1*, R Zeleznik1, J Weiss1, J Taron2, C Hancox1, D Bitterman1, D Kim1, R Punglia1, B Foldyna2, M Lu2, B Kann1, J Bredfeldt1, U Hoffmann2, H Aerts1, R Mak1, (1) Dana-Farber/Brigham and Women's Cancer Center and Harvard Medical School, Boston, MA, (2) Massachusetts General Hospital and Harvard Medical School, Boston, MA, |
| TH-AB-TRACK 4-13 | Attention Guided Lymph Node Malignancy Prediction in Head and Neck Cancer L Chen1*, M Dohopolski1, Z Zhou2, K Wang1, R Wang1, D Sher1, J Wang1, (1) UT Southwestern Medical Center, Dallas, TX, (2) University Of Central Missouri, Warrensburg, MO |
| TH-D-TRACK 5-5 | A Generalizable Contour Validation Method Using Deep Learning-Based Image Classification Y Zhang*, F Ceballos, Y Liang, L Buchanan, X Li, Medical College of Wisconsin, Milwaukee, WI |
| TU-C-TRACK 1-7 | 4D Radiomics: Impact of 4D Image Quality On Radiomic Analysis Z Zhang*, M Huang, Z Jiang, Y Chang, J Torok, F Yin, L Ren, Duke University Medical Center, Cary, NC |
| TU-CD-TRACK 2-1 | Application of Quantum Reinforcement Learning and Deep Neural Network for Radiotherapy Clinical Decision Support D Niraula*, J Jamaluddin, R Ten Haken, I El Naqa, University of Michigan, Ann Arbor, MI |
| TU-CD-TRACK 2-2 | Integrating Multi-Omics Information in Deep Learning Architecture for Joint Actuarial Outcome Prediction in Non-Small-Cell Lung Cancer Patients After Radiation Therapy S Cui*, R Ten Haken, I El Naqa, University of Michigan, Ann Arbor, MI |
| TU-CD-TRACK 2-5 | Comparing V/Q SPECT Dose-Function Metrics with Regard to Radiation-Induced Lung Toxicity in NSCLC Patients Undergoing RT D Owen1*, Y Sun2, B Viglianti3, I El Naqa4, J Balter5, S Jolly6, R Ten Haken7, F Kong8, M Matuszak9, (1) Univ Michigan, Ann Arbor, MI, (2) University Of Michigan Radiation Oncology, ,,(3) ,,,(4) University of Michigan, Ann Arbor, MI, (5) Univ Michigan, Ann Arbor, MI, (6) University of Michigan, Ann Arbor, MI, (7) University of Michigan, Ann Arbor, MI, (8) Case Western Reserve University- School of Medicine, Cleveland, OH, (9) University of Michigan, Ann Arbor, MI |
| TU-D-TRACK 3-5 | NTCP Fitting of Cardiac Toxicity and Dose to Different Parts of Heart After Radiation for Stage III Non-Small Cell Lung Cancer P Mavroidis*, M Eblan, B Jensen, J Dooley, L Marks, S Das, K Wang, Univ North Carolina, Chapel Hill, NC |
| TU-EF-TRACK 3-2 | A Deep-Learning Method of Automatic VMAT Planning Via MLC Dynamic Sequence Prediction (AVP-DSP) Using 3D Dose Prediction: A Feasibility Study of Prostate Radiotherapy Application Y Ni1*, J Zhang2, Y Sheng2, X Li2, J Ye3, Y Ge4, Q Wu2, C Wang2, (1) Duke Kunshan University, Kunshan, 32, CN, (2) Duke University Medical Center, Durham, NC, (3) Swedish Medical Center, Seattle, WA, (4) University of North Carolina at Charlotte, Charlotte, NC, |
| TU-EF-TRACK 3-3 | A Lightweight Deep-Learning Model for Automatic IMRT Planning Via Fluence Map Prediction with a 2.5D Implementation: A Study of Head-And-Neck IMRT Application C Wang1*, X Li1, Y Sheng1, J Zhang1, K Lafata1, F Yin1, Q Wu1, Y Ge2, Q Wu1, (1) Duke University Medical Center, Durham, NC, (2) University of North Carolina at Charlotte, Charlotte, NC |
| TU-EF-TRACK 3-5 | Automation of Palliative Radiotherapy Treatment Planning Using Independent Models to Prevent Errors From Propagating Through the Planning Process T Netherton1*, D Rhee2, C Cardenas3, C Chung4, A Klopp5, L Colbert6, C Nguyen7, V Kolluru8, R Douglas9, C Peterson10, R Howell11, P Balter12, L Court13, (1) MD Anderson Cancer Center, Houston, TX, (2) MD Anderson Cancer Center, Houston, TX, (3) University of Texas MD Anderson Cancer Center, Houston, TX, (4) MD Anderson Cancer Center, Houston, TX, (5) MD Anderson Cancer Center, Houston, TX, AF, (6) Md Anderson Cancer Center, ,,(7) MD Anderson Cancer Center, Houston, TX, (8) Ut Md Anderson Cancer Center, ,,(9) The University Of Texas Md Anderson Cancer Center, ,,(10) The University of Texas MD Anderson Cancer Center, Houston, TX, (11) The University of Texas MD Anderson Cancer Center, Houston, TX, (12) UT MD Anderson Cancer Center, Houston, TX, (13) UT MD Anderson Cancer Center, Houston, TX |
| TU-EF-TRACK 3-7 | BEST IN PHYSICS (THERAPY): Insights Into Planning Techniques Mastered by An Autoplanning Robot: Can An AI Planning Agent Be Interpretable and Tractable? J Zhang1*, C Wang1, Y Sheng1, F Yin1, Y Ge2, Q Wu1, (1) Duke University Medical Center, Durham, NC, (2) The University of North Carolina at Charlotte, Chartlotte, NC |
| TU-EF-TRACK 3-8 | Inter-Institutional Dose Prediction with Deep Convolutional Neural Network and Transfer Learning for Prostate Cancer VMAT Treatments R Norouzi-Kandalan1,2*, D Nguyen1, M Lin1, A Barragan Montero3, S Breedveld4, K Namuduri2, S Jiang1, (1) UTSouthwestern Medical Center, Dallas, TX(2) University of North Texas, Denton, TX (3) Universite Catholique de Louvain, Brussels, VBR, BE (4) Erasmus University Medical Center, RTM, NL |
| TU-EF-TRACK 3-9 | Knowledge-Based Three-Dimensional Dose Prediction for Tandem-And-Ovoid Brachytherapy K Cortes*, A Simon, K Kallis, J Mayadev, S Meyers, K Moore, University of California San Diego, La Jolla, CA |
| TU-EF-TRACK 3-10 | Machine Learning for Lung SBRT Auto Planning and Clinical Decision Support S Zieminski*, H Willers, F K Keane, M Khandekar, Y Wang, Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA |
| TU-EF-TRACK 3-15 | Human Knowledge Augmented Deep Reinforcement Learning for Intelligent Automatic Radiotherapy Treatment Planning C Shen*, L Chen, Y Gonzalez, D Nguyen, S Jiang, X Jia, The University of Texas Southwestern Medical Ctr, Dallas, TX |
| WE-CD-TRACK 2-0 | Advances of Radiomics and Genomics in Cancer Management M Giger1*, J Deasy2*, I Tai3*, F Yin4*, (1) University of Chicago, Chicago, IL, (2) Memorial Sloan Kettering Cancer Center, New York, NY, (3) BCCancer Agency At Vancouver, Vancouver, BC, CA, (4) Duke University Medical Center, Chapel Hill, NC |
| WE-E-TRACK 2-6 | Deciphering Metabolic Features to Target Neuroblastoma Using Machine Learning R Wang1,2,3*, Y Zhang1,4, P Pachnis4, H Vu4, K Wang1,3, R Deberardinis4, J Wang1,3, (1) Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX. (2) School of Artificial Intelligence, Xidian University, Xi'an, People's Republic of China. (3) Medical Artificial Intelligence and Automation (MAIA) Lab, University of Texas Southwestern Medical Center, Dallas, TX. (4) Children's Research Institute, University of Texas Southwestern Medical Center, Dallas, TX. |
| WE-F-TRACK 1-1 | JACK FOWLER JUNIOR INVESTIGATOR COMPETITION WINNER: Joint Adversarial Generator-Segmentor for Unsupervised CT to MRI Synthesis Based MRI Lung Tumor Segmentation J Jiang*, Y Hu, N Tyagi, A Rimner, J Deasy, S Berry, H Veeraraghavan, Memorial Sloan Kettering Cancer Center, New York, NY |
| WE-F-TRACK 1-3 | Accelerating MRI Acquisition Using Cascaded Attention UNet with Prior Information V Agarwal*, J Balter, Y Cao, Univ Michigan, Ann Arbor, MI |