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Keywords: Image Processing
| MO-A-BRC-3 | Inter-Observer variation of Target and Organ Contouring Before and After the Adoption of A deep-Learning Auto-Contouring Model for Localized Prostate Cancer Y Wang*, S C Kamran, J A Efstathiou, Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA |
| MO-A-BRC-4 | Prospective Clinical Experiences of Using the First Pediatric Deep-Learning Auto-Contouring models for Cranio-Spinal Irradiation (CSI) S Zieminski*, S M MacDonald, T I Yock, Y Wang, Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA |
| MO-A-BRC-5 | Quantifying the Impact of Class Imbalance Handling Techniques On Medical Image Deep Learning Performance B Reber*, K Brock, The University of Texas MD Anderson Cancer Center, Houston, TX |
| PO-GePV-I-77 | Feasibility of Implementing Standardized Fluorodeoxyglucose and Florbetapir Brain PET Processing Pipelines M Naseri1,2*, S Ramakrishnapillai2, L Bazzano3, O Carmichael2, (1) Louisiana State University, Baton Rouge, LA, (2) Pennington Biomedical Research Center, Baton Rouge, LA, (3) Tulane School of Public Health and Tropical Medicine, New Orleans, LA, |
| PO-GePV-I-78 | Comparison of 18F-Choline and 18F-PSMA Performance On Detecting Malignant Lesions Using Fuzzy C-Means Segmentation On PET-CT Images of Patients with Recurrent Prostate Cancer M Katsikakis1, I Gatos1, N Papathanasiou2, S Tsantis1, D Apostolopoulos2, K Papadimitropoulos2, M Spiliotopoulou2, E Liatsikos3, D Mihailidis4, G C Kagadis1*, (1) Department of Medical Physics, University of Patras, Rion, GR 26504, GR, (2) Department of Nuclear Medicine, University of Patras, Rion, GR 26504, GR, (3) Department of Urology, University of Patras, Rion, GR 26504, GR, (4) University of Pennsylvania, Wynnewood, PA, USA |
| PO-GePV-M-30 | Scalable De-Identification Pipeline for Radiation Therapy Machine Learning Research D Moseley*, S Seetamsetty, E Tryggestad, S Shiraishi, Mayo Clinic, Rochester, MN |
| PO-GePV-M-47 | IBSI-Compatible Convolutional Image Texture Filters in CERR A Iyer*, E LoCastro, H Veeraraghavan, J Deasy, A Apte, Memorial Sloan Kettering Cancer Center, New York, NY |
| PO-GePV-M-127 | Deep Learning for MRI-Generated Synthetic CT: Dosimetric Evaluation for RT Planning in Head and Neck Cancers JT Antunes1*, D Pittock1, P Jacobs1, AS Nelson1, J Piper1, T Young2,3, S Deshpande3,4 (1) MIM Software Inc, Cleveland, OH, (2) Institute of Medical Physics, School of Physics, University of Sydney, Australia, (3) Liverpool And Macarthur Hospital, Liverpool, Australia, (4) South Western Sydney Clinical School, University of New South Wales, Sydney, Australia |
| PO-GePV-M-136 | Cranial Dose Calculation On Synthetic CT for Gamma Knife Radiosurgery F Li1*, A Xu2, O Dona Lemus3, T Wang4, M Sisti5, C Wuu6, (1) Columbia University, New York, NY, (2) Columbia University Medical Center, New York, NY, (3) University of Rochester, Rochester, NY, (4) Columbia University Medical Center, New York, NY, (5) Columbia University Medical Center, New York, NY, (6) Columbia Univ, New York, NY |
| PO-GePV-M-180 | Multimodality Image Registration and Fusion Using Deep Learning for Radiotherapy Planning A Ratke*1, E Darsht1, C Baeumer2, B Spaan1, K Kroeninger1, (1) TU Dortmund University, Dortmund, DE, (2) West German Proton Therapy Centre Essen, Essen, DE |
| PO-GePV-M-293 | Evaluating the Relationship Between MR Image Quality Measures and Deep Learning-Based Brain Tumor Segmentation Accuracy R Muthusivarajan1*, A Celaya2, J Yung3, S Viswanath4, D Marcus5, C Chung6, D Fuentes7, (1) UT MD Anderson Cancer Center, ,,(2) MD Anderson Cancer Center, Houston, TX, (3) UT MD Anderson Cancer Center, Houston, TX, (4) Case Western Reserve University, ,,(5) ,,,(6) The University of Texas MD Anderson Cancer Center, Houston, TX, (7) UT MD Anderson Cancer Center, Houston, TX |
| PO-GePV-M-309 | Deep Learning-Based Hippocampus Substructure Segmentation Using a Mutually Enhanced Strategy Y Fu, Y Lei, J Roper, J Wolf*, J Bradley, T Liu, H Mao, X Yang, Winship Cancer Institute of Emory University, Atlanta, GA |
| PO-GePV-M-323 | Auto-Segmentation for Limited Field of View CBCT in Male Pelvic Region Using Deep Learning Method H Hirashima1*, M Nakamura2, K Imanishi3, M Nakao4, T Mizowaki5, (1) Kyoto University, Graduate School of Medicine, Department of Radiation Oncology and Image-Applied Therapy, Kyoto, JP, (2) Kyoto University, Graduate School of Medicine, Department of Human Health Sciences, Kyoto, JP, (3) e-Growth Co., Ltd., Hyogo, JP, (4) Kyoto University, Graduate School of Informatics, Department of Systems Science, Kyoto, JP,(5) Kyoto University, Graduate School of Medicine, Department of Radiation Oncology and Image-Applied Therapy, Kyoto, JP |
| PO-GePV-M-327 | Inpainting Truncated Areas of CT Images Based On Generative Adversarial Networks with Gated Convolution for Radiotherapy X Kai1,2*, X Qianyi2,3, G Liugang1,2, J Sun1,2, C Qian1,2, N Xinye1,2,3, (1) The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, Jiang Su, CN, (2) Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, Jiangsu, CN,(3) Center for Medical Physics, Nanjing Medical University, Changzhou, Jiangsu, CN |
| PO-GePV-M-349 | Quantitative Evaluation of Radiodermatitis Following Whole-Breast Radiotherapy with Various Color Space Models: A Feasibility Study S Park1*, J Kim2, C Choi2, J Park2, J Kim2, (1) Veterans Health Service Medical Center, Seoul, KR, (2) Seoul National University Hospital, Seoul, KR |
| PO-GePV-T-76 | A Fast and Accurate Contour Tracing (FACT) Method for Custom Electron Cutout Using a Beam’s Eye View (BEV) Camera J Sohn1,2*, J Park3, S Kim1, (1) Virginia Commonwealth University, Richmond, VA, (2) Northwestern University Feinberg School of Medicine, Chicago, IL (3) Memorial Sloan Kettering Cancer Center, Basking Ridge, NJ |
| SU-E-201-4 | Deep-Learning-Based Auto Segmentation (DLAS) of Organs at Risk in the Head and Neck Region: A Clinical Evaluation C Johnson*, P Tsai, G Yu, C Apinorasethkul, L Hu, W Xiong, A Zhai, R Press, H Lin, S Huang, New York Proton Center, New York, NY |
| SU-E-BRB-5 | Comparing Transfer Learning, Data Augmentation, and Data Expansion in the Improvement of Medical Image Generation M Woodland1,2*, J Wood1, B Anderson4, S Kundu1, E Lin1, E Koay1, B Odisio1, C Chung1, H Kang1, A Venkatesan1, S Yedururi1, B De1, Y Lin1, A Patel2,3, K Brock1, (1) The University of Texas M.D. Anderson Cancer Center, Houston, TX, (2) Rice University, Houston, TX, (3) Baylor College of Medicine, Houston, TX, (4) University of California San Diego, San Diego, CA |
| SU-F-207-4 | Accelerated MRI Reconstruction Using Variational Feedback U-NET with Transfer Learning Y Zhou1*, R Paul2, P Ding2, R Battle1, A Patel1, B Li2, (1) Mayo Clinic Arizona, Phoenix, AZ, (2) CIDAS, Arizona State University, Tempe, Az, United States |
| SU-F-BRB-3 | Automatic Image and Contour Augmentation for Deep Learning Auto-Segmentation of Complex Anatomy N Dang*, Y Zhang, A Amjad, J Ding, C Sarosiek, X Li, Medical College of Wisconsin, Milwaukee, WI |
| SU-H300-IePD-F5-3 | Deep Learning Based 4D Synthetic CTs Generated From CBCTs for Proton Dose Calculations in Adaptive Proton Therapy A Thummerer1*, C Seller Oria1, S Visser1, P Zaffino2, A Meijers3, R Wijsman1, G Guterres Marmitt1, J Seco4,5, J Langendijk1, A Knopf1,6, M Spadea2, S Both1, (1) Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen,NL, (2) Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro,IT, (3) Center for Proton Therapy, Paul Scherrer Institute, Villigen, CH, (4) DKFZ Heidelberg, Heidelberg, DE, (5) Department of Physics and Astronomy, Heidelberg University, Heidelberg, DE (6) Department I of Internal Medicine, Center for Integrated Oncology Cologne, University Hospital of Cologne, Cologne, DE |
| SU-H300-IePD-F9-2 | Developing Phenotypic-Specific Brain Templates for DTI-Based Analysis L LeMerise1*, J Guerrero2, S Hartley3, A Alexander4, B Christian5, (1) University of Wisconsin-Madison, Madison, WI, (2) University of Wisconsin Madison, Madison, WI, (3) University Of Wisconsin-Madison, Madison, WI,(4) University of Wisconsin - ADCL, Madison, WI, (5) University of Wisconsin, Madison, WI |
| SU-I400-BReP-F2-3 | Patient-Specific Image Prior Assisted Fast MR Imaging for Online Adaptive Radiotherapy Y Gao*, C Shen, Y Gonzalez, J Deng, X Jia, University of Texas Southwestern Medical Center, Dallas, TX |
| SU-J-201-7 | Patient Vs. Phantom: The Impact of Post-Processing On Image Quality of Chest Radiographs V Yadav*, E Macdonald, N Lafata, J Wilson, E Samei, Duke University Health System, Durham, NC |
| SU-J-207-3 | Boundary-Constrained Neural Network for Mouse Organ Segmentation L Jiang*, Q Xu, A Chatziioannou, K Sheng, University of California Los Angeles, Los Angeles, CA |
| TH-B-202-3 | Performance Evaluation of a GE Discovery NM/CT 870 DR and a GE Discovery 670 CZT for Radium-223 Digital SPECT/CT Imaging A Kulkarni1,2*, L Zimmermann1,2, P Wojtylak1, S Deng1,2, R Al Helo1,2, N A Harris1,2, D Jordan1,2,3, A Kardan1, (1) Dept. of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, (2) Radiation Safety, University Hospitals Cleveland Medical Center, Cleveland, OH, (3) Dept. of Radiology, Case Western Reserve University, Cleveland, OH |
| TH-B-207-6 | Segmentation of High-Resolution Blood Vasculature Trees in CT D Yang1*, Y Hao2, Y Duan3, (1) Duke University, Chapel Hill, NC, (2) Washington University School of Medicine, St. Louis, MO, (3) University Of Missouri, |
| TH-D-207-3 | Deep Learning Prostate Segmentation in 3D Ultrasound and the Impact of Image Quality and Training Dataset Size N Orlando1,2*, I Gyacskov2, D Gillies3, D Cool1,3, D Hoover1,3, A Fenster1,2, (1) Western University, London, ON, CA, (2) Robarts Research Institute, London, ON, CA, (3) London Health Sciences Centre, London, ON, CA |
| TH-F-BRC-5 | MUsculo-Skeleton-Aware Deep Learning-Based Deformable Registration for Head-And-Neck CT with a Relaxed Rigidity Constraint On Bony Structures H Liu1,2*, E McKenzie3, Q Xu1,2, D Ruan1,2, K Sheng1,2, (1) UCLA, Los Angeles, CA, (2) UCLA School of Medicine, Los Angeles, CA, (3) Cedars-Sinai Medical Center, Los Angeles, CA |
| TU-E-201-2 | Deep-Unfolding-Network-Based Non-Blind Deblurring for Fast-Rotating Wide-Angle Digital Breast Tomosynthesis S Hyun*, S Lee, H Kim, S Cho, Korea Advanced Institute of Science and Technology, Daejeon, 44KR, |
| TU-GH-BRB-3 | Augmented Colorectal Cancer Detection Using Self-Attention-Incorporated Deep Learning X Jia1, S Sang1, Y Zhou2, H Ren1, M Laurie1, M Islam1, O Eminaga1, J Liao1, L Xing1*, (1) Stanford University School of Medicine, Stanford, CA, (2) University of California Santa Cruz, Santa Cruz, CA |
| WE-B-201-1 | Deep-Learning Based Rectal Tumor Localization and Segmentation On Multi-Parametric MRI Y Zhang1*, S Hu1, L Shi2, X Sun2, N Yue1, K Nie1, (1) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (2) Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang Univ., Hangzhou, CN |
| WE-C1000-IePD-F6-4 | PET Image Quality Enhancement for RefleXion X1 Biology-Guided Radiotherapy (BGRT) System Using Patient-Specific Mean Teacher UNet (MT-UNet) J Fu1*, Z Zhang1, L Shi1, Z Hu2, P Dong1, G Pratx1, L Vitzthum1, D Chang1, L Xing1, W Liu1, (1) Stanford Radiation Oncology, Stanford, CA, (2) Reflexion Medical, Inc., Union City, CA |
| WE-G-BRC-6 | A Deep Learning U-Net Based Model to Automatically Correct Inaccurate Auto-Segmentation for MR-Guided Adaptive Radiotherapy J Ding*, Y Zhang, A Amjad, C Sarosiek, N Dang, X Li, Medical College of Wisconsin, Milwaukee, WI |