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| MO-E115-GePD-F5-3 | Respiratory Deformation Registration in 4D-CT/CBCT Using Deep Learning X Teng1*, Y Chen2 , Z Jiang2 , Y Zhao1, L Ren2 , (1) Duke Kunshan University, Kunshan, (2) Duke University, Durham, NC |
| MO-K-SAN4-0 | Introduction to Scientific Computing for Medical Physics Research S Hedrick1*, G Sharp2*, E Schreibmann3*, (1) Provision Center for Proton Therapy, Knoxville, TN, (2) Massachusetts General Hospital, Boston, MA, (3) Emory Univ, Atlanta, GA |
| PO-GePV-M-21 | Extracting Heterogeneously Formatted Clinical Data From DICOM Secondary Capture Using OCR E Somasundaram*, S Brady , H Li , L He , T Maloney , J Dudley , J Dillman , Cincinnati Childrens Hospital Med Ctr, Cincinnati, OH |
| SU-E-225BCD-1 | 3D, CT-Free Breast Treatment Planning Simulation for Low- and Middle-Income Countries: Proof of Concept Y Roed1*, R Cardan2 , S Shaitelman1 , D Craft3 , R Howell1 , A Jhingran1 , R Popple2 , L Court1 , (1) UT MD Anderson Cancer Center, Houston, TX, (2) UAB University of Alabama, Birmingham, AL, (3) Mayo Clinic, Phoenix, AZ |
| SU-E-SAN2-2 | Comparison in Classification Performance of Radiation Pneumonitis Between Two Delta Radiomics Logistic Regression Models J Foy*, H Al-Hallaq , S Armato , The University of Chicago, Chicago, IL |
| SU-F-225BCD-6 | Using Neural Net Object Detection for Fiducial Tracking D Sawkey1*, H Mostafavi1, L Cervino2, (1) Varian Medical Systems, Palo Alto, CA (2) UC San Diego, La Jolla, CA |
| SU-G300-SPS-F4-7 | Prediction of Acute Xerostomia in Nasopharyngeal Cancer for Radiotherapy Using 3D Convolutional Neural Network Y LIU1*, X CHEN2 , s Huang3 , H SHI4 , H ZHOU5 , H CHANG6 , Y XIA7 , X Yang8 , (1) School of Software Engineering, South China University of Technology, Guangzhou, ,(2) School of Software Engineering, South China University of Technology, Guangzhou,(3) State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, (4) School of Software Engineering, South China University of Technology, Guangzhou, ,(5) the 74th Group Army Hospital of the Chinese People's Liberation Army, Guangzhou, ,(6) SYSUCC, Guangzhou, ,(7) SYSUCC, Guangzhou, ,(8) Sun Yat-Sen University Cancer Center (SYSUCC), Guangzhou City |
| SU-I430-GePD-F9-3 | Initial Evaluation of the Use of a Convolutional Neural Network to Determine Coronary Artery Disease Severity Using Computed Tomography Angiography A Podgorsak1, 2*, K Sommer1, 2 , V Iyer3 , M Wilson1 , U Sharma1 , K Kumamaru3 , F Rybicki4 , D Mitsouras4 , E Angel5 , C Ionita1, 2 , (1) SUNY Buffalo, Buffalo, NY, (2) Canon Stroke and Vascular Research Center, Buffalo, NY, (3) Juntendo University, Tokyo, (4) University of Ottawa, Ottawa, ON, (5) Canon Medical Systems, Tustin, CA |
| SU-K-221CD-1 | A Deep-Learning Based Lower-Dose CT Simulation Technique in Image Domain H Gong*, S Leng , C McCollough , L Yu , Mayo Clinic, Rochester, MN |
| SU-K-303-1 | Auto Segmentation of Organs at Risk in Thorax Computed Tomography Using Deep Convolutional Neural Networks M Haytmyradov1*, M Surucu1 , F Cassetta1 , J Roeske1 , (1) Loyola University Medical Center, Maywood, IL |
| TH-A-225BCD-2 | Improving Deformable Image Registration of 4DCTs Using a Generative Adversarial Deep Neural Network for Automated Lung Lobe Segmentation B Stiehl1*, M Lauria1 , I Barjaktarevic1 , P Lee1 , D Low1 , A Santhanam1 , (1) University of California, Los Angeles, Los Angeles, CA |
| TH-D-302-4 | Automatic Detection of Non-Diagnostic Pediatric Lateral Airway/Soft Tissue Neck Radiographic Exams Using Deep Learning at the Point of Care E Somasundaram1*, J Dillman2 , E Crotty3 , B Coley4 , S Brady5 , (1) Cincinnati Children's Hospital Medical Center, Cincinnati, OH, (2) Cincinnati Children's Hospital, Cincinnati, OH, (3) ,Cincinnati, ,(4) Cincinnati Children's Hospital and Medical Center, Cincinnati, ,(5) Cincinnati Childrens Hospital Med Ctr, Cincinnati, OH |
| TU-AB-SAN2-4 | Multi-Branch Convolutional Neural Network Combines Unregistered PET and CT Images for Head & Neck Cancer Outcome Prediction A Diamant*, A Chatterjee , M Vallieres , G Shenouda , J Seuntjens , McGill University Health Centre, Montreal, QC |
| TU-AB-SAN2-12 | Identifying Oropharyngeal Clinical Target Volumes Delineation Patterns From Peer-Reviewed Clinical Delineations Via Cascade 3D Fully-Convolutional Networks C Cardenas1*, J Yang1 , A Mohamed1 , C Fuller1 , B Beadle2 , A Garden1 , L Court1 , (1) University of Texas MD Anderson Cancer Center, Houston, TX, (2) Stanford University, Stanford, CA |
| TU-C930-GePD-F6-6 | Understanding the Impact of Heterogeneous Iterative Reconstruction and Dose Conditions in Low-Dose CT Computer-Aided Detection of Lung Nodules M Wahi-Anwar*, N Emaminejad , G Kim , M McNitt-Gray , M Brown , David Geffen School of Medicine at UCLA, Los Angeles, CA |
| TU-E-SAN2-6 | Standardizing Structure Labels in Head-And-Neck Region Using Non-Local 3D-ResNet Q Yang1,2*, T Rozario1, A Barragan Montero1, M Joo1, R McBeth1, H Chao1,2, S Jiang1, (1) Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA, (2) School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, P.R.China |
| TU-L-304-7 | Standardization in Quantitative Imaging: A Comparison of Radiomics Feature Values Obtained by Different Software Packages On a Set of Digital Reference Objects M McNitt-Gray1*, S Napel2 , J Kalpathy-Cramer3 , A Jaggi2 , D Cherezov4 , D Goldgof4 , H Yang5 , E Jones6 , M Muzi7 , N Emaminejad1 , M Wahi-Anwar1 , Y Balagurunathan8 , M Abdalah8 , B Zhao5 , L Hadjiiski9 , L Pierce7 , K Farahani10 , (1) David Geffen School of Medicine at UCLA, Los Angeles, CA, (2) Stanford Univ School of Medicine, Stanford, CA, (3) Massachusetts General Hospital, Boston, MA, (4) University of South Florida, Tampa, FL, (5) Columbia University, New York, NY, (6) UCSF, San Francisco, CA, (7) University of Washington, Seattle, WA (8) Moffitt Cancer Center, Tampa, FL, (9) University of Michigan, Ann Arbor, MI, (10) National Cancer Institute, Bethesda, MD |
| WE-C1030-GePD-F2-5 | Deep Learning for Automatic Real-Time Pulmonary Nodule Detection and Quantitative Analysis C Liu1*, S Hu2 , F Yin3 , (1) Duke Kunshan University, Suzhou, Jiangsu, (2) Duke Kunshan University, Suzhou, Jiangsu, (3) Duke University Medical Center, Durham, NC |
| WE-FG-304-3 | Computer-Aided Diagnosis for Wireless Capsule Endoscopy Based On Convolutional Neural Networks: Experimental Feasibility and Optimization On a Large Dataset S Wang1*, Y Xing2 , L Zhang3 , H Gao4 , H Zhang5 , (1) Tsinghua University, Beijing, ,(2) Tsinghua University, Beijing, ,(3) Tsinghua University, Beijing, 11, (4) Tsinghua University, Beijing, ,(5) Ankon Technologies Co, Ltd (Wuhan, Shanghai, China), Wuhan, |
