DISCLAIMER:
Entry of taxonomy/keywords during proffered abstract submission was optional.
Not all abstracts will appear in search results.
Keywords: Segmentation
| MO-B-BRC-1 | Clinical Applicability-Oriented Contour Quality Classification for Auto-Segmentation Y Zhang*, J Ding, A Amjad, C Sarosiek, N Dang, W Hall, B Erickson, X Li, Medical College of Wisconsin, Milwaukee, WI |
| MO-B-BRC-3 | Deep Learning-Guided Iterative Refinement to Improve Label Quality and Consistency H Zhou1*, J Xiao2, Z Fan2, 3, 4, D Ruan1, 5, (1) Department of Bioengineering, University of California, Los Angeles, CA 90095, (2) Department of Radiology, University of Southern California, Los Angeles, CA 90033, (3) Department of Radiation Oncology, University of Southern California, Los Angeles, CA 90033, (4) Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089 (5) Department of Radiation Oncology, University of California, Los Angeles, CA 90095 |
| MO-B-BRC-4 | Patient-Specific Transfer Learning to Enhance the Performance of Deep Learning Auto-Segmentation in 0.35 T MRgRT for Prostate Cancer M Kawula1*, I Hadi1, L Nierer1, M Vagni2, D Cusumano2, L Boldrini2, L Placidi2, S Corradini1, C Belka1,3, G Landry1, C Kurz1, (1) Department of Radiation Oncology, University Hospital, LMU Munich, Germany, (2) Fondazione Policlinico Universitario Agostino Gemelli, Rome, Italy, (3) German Cancer Consortium (DKTK), Munich, Germany |
| MO-C930-IePD-F9-3 | Automatic Organ Contouring for Head and Neck MR-Guided Online Adaptive Radiotherapy Using Neural Networks V Koteva1, D Mcquaid2, A Dunlop3, B Eiben4, E Persson5*, U Oelfke6, (1) The Institute of Cancer Research, Iver, BKM, GB, (2) Royal Marsden Hospital, ,,(3) Royal Marsden Hospital, ,,(4) The Institute Of Cancer Research, (5) Sutton, GB, (6) The Institute of Cancer Research, Sutton |
| MO-E115-IePD-F7-5 | Can Image Segmentation Evaluation Metrics Be Used for Assessing the Quality of SBRT Plans? T Arsenault1*, A Amini1, A Baydoun1, B George2, L Bailey2, S Bhat2, R Kashani3, T Podder3, (1) University Hospitals Cleveland Medical Center, Cleveland, OH, (2) Case Western Reserve University, School Of Medicine,(3) Seidman Cancer Center /Uh Cleveland & CWRU, OH, Cleveland, OH |
| MO-E115-IePD-F8-3 | Leveraging Predictive Uncertainty of a Convolutional Neural Network to Flag Unacceptable Segmentations Z Klanecek1*, H Bosmans2,6, A Studen1,5, M Vrhovec4, D Huff3, B Schott3, Y Kuan2, L Cockmartin6, N Marshall2,6, T Wagner2, K Hertl4, M Krajc4, K Jarm4, R Jeraj1,3,5, (1) Faculty of Mathematics and Physics, Ljubljana, SI, (2) KU Leuven, Leuven, BE, (3) University of Wisconsin - Madison, Madison, WI, (4) Institute Of Oncology Ljubljana, Ljubljana, SI, (5) Jozef Stefan Institute, Ljubljana, SI, (6) UZ Leuven, Leuven, BE |
| MO-E115-IePD-F9-1 | Validation of a Commercial Artificial Intelligence Auto-Segmentation System for Head and Neck Treatment Site P Tsai, S Huang*, R Press, A Shim, C Apinorasethkul, C Chen, L Hu, E Jang, H Lin, New York Proton Center, New York, NY |
| MO-FG-BRB-2 | A Neural Ordinary Differential Equation Model for Visualizing Deep Neural Network Behaviors in Multi-Parametric MRI Based Glioma Segmentation Z Yang1*, Z Hu2, H Ji3, K Lafata1, S Floyd1, F Yin1, C Wang1, (1) Duke University, Durham, NC, (2) Duke Kunshan university, Kunshan, Jingsu, China, (3) North Carolina State University, Raleigh, NC |
| PO-GePV-I-9 | Segmentation of Tumor and Organs at Risk for CBCT-Based Online Adaptive Radiotherapy Using Recurrent Neural Networks with Multi-Scale Memory H Zhao*, X Liang, B Meng, M Dohopolski, B Choi, B Cai, M Lin, T Bai, D Nguyen, S Jiang, UTSW, Dallas, TX |
| PO-GePV-I-20 | Real-Time 3D CT Reconstruction and Tumor Segmentation Based On Single X-Ray Image at Any Gantry Angle M Zhang1, B Liu1,2*, R Wei3, F Zhou1,2, (1) Image Processing Center, Beihang University, Beijing,100191,CN, (2) Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing,100083,CN(3) National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Beijing,100021,CN |
| PO-GePV-I-51 | Development and Verification of a Subregional Radiomic Framework for Image Segmentation J Gu1*, B Li1, J Zhu2, (1) Southeast University, Nanjing, JiangSu, CN, (2) Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, CN |
| PO-GePV-I-53 | MATU-Net: A Deep Learning-Based Pipeline for Auto Segmentation of Pelvic Lymph Nodes During Brachytherapy of Locally Advanced Cervical Cancer E Showkatian1*, Z Siavashpour2, S Sadeghi3, (1) Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran, (2) Department of Radiotherapy Oncology, Shohada-e Tajrish Medical Center, School of Medicine, Shahid Beheshti University of Medical Science, Tehran, Iran, (3) Department of Nuclear Physics, Faculty of Sciences, University of Mazandaran, Babolsar, Iran |
| PO-GePV-I-64 | Assessment of the Generalizability of Organ Segmentation CNNs Across CT Scanner Manufacturers A Weisman*, M La Fontaine, O Lokre, R Munian-govindan, T Perk, AIQ Solutions, Madison, WI |
| 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-I-100 | Attention U-Net Segmentation of Indeterminate Nodules On Thyroid Ultrasound J Genender*1, J Fuhrman1, H Li1, Kelvin Memeh1, J Conn Busch1, J Williams1, L Lan1, D Sarne1, B Finnerty2, P Angelos1, T Fahey2, X Keutgen1, M Giger1 (1) University of Chicago, Chicago, IL, (2) Weill Cornell Medicine, New York, NY |
| PO-GePV-M-5 | Geometric and Dosimetric Evaluation of An Atlas - Based Autosegmentation Software On Organs-At-Risk (OARs) for Tomotherapy Planning of Nasopharyngeal Carcinoma (NPC) Patients Y Wang1*, P Cheung1, J Lui2, F Lee2, L Wing Sum2, H Yiu2, J Cai1, (1) The Hong Kong Polytechnic University, Hong Kong, CN, (2) Queen Elizabeth Hospital, Hong Kong, HK |
| PO-GePV-M-7 | Performance Evaluation of AI-Based Automatic Segmentation Modules for Head and Neck Cancer Patients Y Liao*, R Injerd, G Tolekidis, N Joshi, J Turian, Rush University Medical Center, Chicago, IL |
| PO-GePV-M-12 | Correlations of Evaluation Metrics in Deep Learning-Based Segmentation H Tan*, Y Xiao, R McBeth, S Lee, University of Pennsylvania, Philadelphia, PA |
| PO-GePV-M-13 | Exploring the Combination of Deep-Learning Based Direct Segmentation and Deformable Image Registration for Cone-Beam CT Based Auto-Segmentation for Adaptive Radiotherapy X Liang*, H Morgan, T Bai, M Dohopolski, D Nguyen, S Jiang, University of Texas Southwestern Medical Center, Dallas, TX |
| PO-GePV-M-15 | A Clinical-Friendly Deep Interactive Segmentation Algorithm for Volumetric Image T Bai1*, M Lin1, X Liang1, B Wang1,2, M Dohopolski1, B Cai1, D Nguyen1, S Jiang1, (1) The University of Texas Southwestern Medical Ctr, Dallas, TX, (2) Southern Methodist University, Dallas, TX |
| PO-GePV-M-16 | Evaluation of a Commercial Convolution Neural Network Based Auto-Segmentation Software M Leyva*, D Wang, N Mcallister, A Gutierrez, R Tolakanahalli, Miami Cancer Institute, Miami, Florida |
| PO-GePV-M-35 | Convolutional Neural Networks for the Automated Segmentation of Malignant Pleural Mesothelioma: Analysis of Performance Based On Probability Map Threshold M Shenouda1*, E Gudmundsson2, F Li1, C Straus1, H Kindler1, A Dudek3, T Stinchcombe4, X Wang4, A Starkey1, S Armato1, (1) The University of Chicago, Chicago, IL, (2) UCL Hospitals NHS Trust, UK, (3) Health Partners Institute, HealthPartners Cancer Care Center, St. Paul, MN, (4) Duke University, Durham, NC |
| PO-GePV-M-42 | A Partial Convolution Generative Adversarial Network to Synthesize Liver Lesions for Enhanced Tumor Segmentation Y Liu1, F Yang2, Y Yang1*, (1) University of Science and Technology of China, Hefei, China, (2) University of Miami, Miami, FL, USA |
| PO-GePV-M-63 | Investigating the Expected Impact of Auto-Contouring in Clinical Practice: A Cohort Analysis D Boukerroui1, J Baker2,3*, Y Mcquinlan1, A Riegel2,3, Y Cao2,3, M Gooding1, L Potters2,3, (1) Mirada Medical Ltd., Oxford, U.K, (2) Department of Radiation Medicine, Northwell Health, Lake Success, NY 11042, (3) Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549 |
| PO-GePV-M-64 | Investigating Disease-Stage Dependence of MR-Based Radiomic Feature Reproducibility Against Image Perturbations in Nasopharyngeal Carcinoma T Cheung, S Lam, J Zhang*, J Cai, Hong Kong Polytechnic University, Hong Kong, CN |
| PO-GePV-M-67 | Impact of Training with Data From Multiple Disease Types On Lesion Detection Performance in Two CNN Architectures A Weisman*, M La Fontaine, O Lokre, R Munian-govindan, T Perk, AIQ Solutions, Madison, WI |
| PO-GePV-M-76 | Evaluation of Deep Learning Based Auto-Contouring of Liver Segments A Gupta*, B Rigaud, S Yedururi, E Kirimli, C Yu, Y He, G Cazoulat, C Oconnor, B Odisio, E Koay, K Brock, The University of Texas MD Anderson Cancer Center, Houston, TX |
| PO-GePV-M-110 | Assessing the Accuracy of Target Contours Derived From Ethos ICBCT Images for Online Adaptive Prostate SBRT D O'Connell*, M Xiang, T Ma, S Yoon, L Valle, R Savjani, J Lamb, University of California, Los Angeles, Los Angeles, CA |
| PO-GePV-M-201 | Automatic Segmentation of Rectal Tumor On Magnetic Resonance Images Via A Deep Discriminative Model Consisting of U-Net and Conditional Random Field Based Post-Processing Hao CHEN1*, Xing Li2, X. Sharon Qi3, (1) Xi'an University of Posts and telecommunications, Xian, ,CN, (2) Xi'an University of Posts and Telecommunications, Xian, Shaanxi Province, CN, (3) UCLA School of Medicine, Los Angeles, CA |
| PO-GePV-M-202 | A Clinical and Time Savings Evaluation of a Commercial Deep Learning Automatic Contouring Algorithm J Ginn1*, H Gay2, J Hilliard2, J Shah3, N Mistry3, C Mohler3, G Hugo2, Y Hao2, (1) Duke University, Durham, NC, (2) Washington University School of Medicine, St. Louis, MO (3) Siemens Healthineers, Durham, NC |
| PO-GePV-M-203 | Automated Segmentation of Vestibular Schwannomas From MRI with Deep Neural Network H Wang*, T Qu, K Bernstein, D Barbee, D Kondziolka, NYU Langone Medical Center, New York, NY |
| PO-GePV-M-207 | Automated Framework for Qualitative Review of Autosegmented Organs-At-Risk E Tryggestad1*, A Anand2, R Foote1, O Foss3, T Hodge1, A Hunzeker1, D Moseley1, A Ridgway2, L Undahl1, S Patel2, (1) Mayo Clinic, Department of Radiation Oncology, Rochester, MN, (2) Mayo Clinic, Department or Radiation Oncology, Phoenix, AZ, (3) Mayo Clinic, Center for Digital Health, Rochester, MN. |
| PO-GePV-M-208 | Quantitative Analysis of Artificial Intelligence-Generated Contours In Comparison With Physician-Generated Contours for Patients with Cancer of The Brain W Godwin*, D McDonald, J Peng, M Maynard, G Owen, A Rapchak, S Roles, J Winiecki, Medical University of South Carolina, Mount Pleasant, SC |
| PO-GePV-M-209 | Automated Clinical Target Volume (CTV) Delineation Using Deep 4D Neural Networks with Enhanced OAR Sparing in Radiation Therapy of Non-Small Cell Lung Cancer (NSCLC) Y Xie1*, K Kang2, Y Wang3, M Khandekar4, H Willers5, F Keane6, T Bortfeld7, (1) Massachusetts General Hospital, Boston, MA, (2) Independent Researcher, ,,(3) Massachusetts General Hospital, Boston, MA, (4) Massachusetts General Hospital, ,,(5) Massachusetts General Hospital, Boston, MA, (6) Massachusetts General Hospital, ,,(7) Massachusetts General Hospital, Boston, MA |
| PO-GePV-M-211 | Clinical Equivalency of Alternate Head-And-Neck OAR Delineations MNH Rashad1*, F Badry1, V Leandro Alves1, H Nourzadeh2, W Choi2, J Siebers1, (1) University of Virginia, Charlottesville, VA, (2) Thomas Jefferson University, Philadelphia, PA, |
| PO-GePV-M-248 | Automatic Segmentation of Salivary Glands and Skeletal Muscles for MR-Based Daily Adapted Head and Neck Cancer Radiotherapy C Cardenas1*, T Ermongkonchai2, D Xing3, Z Iqbal1, A Mcdonald1, A Mohamed4, B Harris3, R Khor3, H Bahig5, C Fuller4, S Ng3, (1) The University of Alabama at Birmingham, Birmingham, AL, (2) University Of Melbourne, Melbourne, Australia,(3) Austin Health, Melbourne, Australia, (4) UT MD Anderson Cancer Center, Houston, TX, (5) CHUM |
| PO-GePV-M-278 | Evaluation of Auto Contouring Accuracy for a Commercial Software Compared to Physician Contouring X Liu1*, Y Zheng1, (1) Guangzhou Concord Cancer Center, Guangzhou, 44, CN |
| PO-GePV-M-291 | A Semi-Supervised Learning Method Using Soft-Label for Cell Nuclei Segmentation On Immunohistochemistry Images J Zhou1*, Z Yan2, J Polf3, H Zhang4, B Zhang5, M MacFarlane6, D Han7, M Zakhary8, A Gopal9, J Xu10, S Lee11, H Xu12, G Lasio13, S Chen14, (1) University of Maryland Shore Medical Center at Easton, Easton, MD, (2) Sense Brain, Princeton, NJ,(3) University of Maryland School of Medicine, Baltimore, MD, (4) University of Maryland School of Medicine, Baltimore, MD, (5) University of Maryland School of Medicine, Baltimore, MD, (6) University of Maryland School of Medicine, Baltimore, MD, (7) University of Maryland School of Medicine, Baltimore, MD, (8) University of Maryland School of Medicine, Baltimore, MD, (9) University of Maryland School of Medicine, Baltimore, MD, (10) University of Maryland School of Medicine, Baltimore, MD, (11) University of Maryland School of Medicine, Baltimore, MD, (12) University of Maryland School of Medicine, Baltimore, MD, (13) University of Maryland School of Medicine, Baltimore, MD, (14) University of Maryland School of Medicine, Baltimore, MD |
| PO-GePV-M-294 | Cross-Comparison of Multi-Platform AI Auto-Segmentation Tools Using Independent Multi- Institutional Datasets for Head and Neck Cancer Y Rong1*, Q Chen2, L Yuan3, X Qi4, K Latifi5, X Yang6, B Cai7, H Al-Hallaq8, Q Wu9, Y Xiao10, S Benedict11, (1) Mayo Clinic Arizona, Phoenix, AZ, (2) City of Hope Medical Center, Duarte, CA, (3) Virginia Commonwealth University Medical Center, Richmond, Virginia, (4) UCLA School of Medicine, Los Angeles, CA, (5) H. Lee Moffitt Cancer Center, Tampa, FL, (6) Emory University School of Medicine, Atlanta, GA, (7) University of Texas Southwestern Medical Center, Clayton, MO, (8) The University of Chicago, Chicago, IL, (9) Duke University Medical Center, Chapel Hill, NC, (10) University of Pennsylvania, Philadelphia, PA, (11) UC Davis Cancer Center, Davis, CA |
| PO-GePV-M-298 | Multi-Site Evaluation of the AI-Assisted Auto Segmentation Quality for a CBCT Based Online Adaptive Radiotherapy System B Meng*, M Dohopolski, S Jiang, M Lin, B Cai, University of Texas Southwestern Medical Center, Dallas, TX |
| PO-GePV-M-308 | Cardiac Substructure Segmentation Using Self-Configuring NnUNet and NnFormer for Cardiac-Sparing Lung Cancer Radiotherapy S Lee1, D Wang1, J Natarajan2, N Yegya-raman1, T Kegelman1, S Feigenberg1, G Kao1, Y Xiao1*, (1) Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, (2) Drexel University College of Medicine, Philadelphia, PA |
| PO-GePV-M-322 | Deep Learning Based Patient-Specific Auto-Segmentation of Target and Organs at Risk On Daily Fan-Beam CT Images Y Chen*, S Butler, L Yu, Y Zhou, L Shen, N Kovalchuk, H Bagshaw, M Gensheimer, L Xing, B Han, Stanford University School of Medicine, Stanford, CA |
| 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-345 | Automatic Segmentation Algorithms Improve the Efficiency and Inter-Observer Agreement of Gross Target Volumes Contours E Czeizler1, J Pajula2, H Polonen3, K Antila4, K Lehtio5, J Lehto6, A Tiulpin7, A Maslowski8, M Hakala9, E Kuusela10, K Bush11*, (1) Varian Medical Systems Finland, Helsinki, FI, (2) VTT Technical Research Center of Finland, Tampere, ,FI, (3) VTT Technical Research Center of Finland, Tampere, FI, (4) VTT Technical Research Center of Finland, Tampere, FI, (5) Oulu University Hospital, Department of Oncology and Radiotherapy, Oulu, FI, (6) Oulu University Hospital, Department of Oncology and Radiotherapy, Oulu, ,FI, (7) Ailean Technologies Oy, FI, (8) Varian Medical Systems, Palo Alto, CA, (9) Varian Medical Systems Finland, Helsinki, FI, (10) Varian Medical Systems Finland, Helsinki, FI, (11) Stanford University, Stanford, CA |
| PO-GePV-T-1 | An Evaluation of Five Commercially Available Models for Autosegmentation of Organs at Risk in Genitourinary Malignancies S Yaddanapudi1*, A Anand2, J Brooks3, M Fatyga2, R Foote3, D Hobbis2, A Jackson1, J Lucido3, J Ma3, D Moseley3, D Pafundi1, S Patel2, Y Rong2, D Routman3, B Stish3, E Tryggestad3, (1) Mayo Clinic, Jacksonville, FL, (2) Mayo Clinic, Phoenix, AZ, (3) Mayo Clinic, Rochester, MN |
| PO-GePV-T-36 | Deep-Learning-Based Automated HDR Applicator Digitization for GYN Brachytherapy S Momin1*, Y Lei2, T Wang3, M Axente4, J Roper5, J Shelton6, J Bradley7, T Liu8, X Yang9, (1) Emory Univ, Decatur, GA, (2) Emory University, Decatur, GA, (3) Emory University, Atlanta, GA, (4) Emory University, Atlanta, GA, (5) Winship Cancer Institute of Emory University, Atlanta, GA, (6) Emory University, ,,(7) Emory University School of Medicine, ,,(8) Emory University School of Medicine, Atlanta, GA, (9) Emory University School of Medicine, Atlanta, GA |
| PO-GePV-T-47 | Evaluate Atlas-Based Auto-Segmentation in MR Images for Liver Yttrium-90 Selective Internal Radiation Therapy J Li*, R Anne, Thomas Jefferson University, Philadelphia, PA |
| PO-GePV-T-92 | Feasibility Study of a Deep Learning-Based Model Trained On Adults for Auto-Segmentation of Head-And-Neck OARs in Pediatric CT Images Z Shen1*, A Olch2, N Bai3, K Wong2, E Chang1, W Yang1, (1) University of Southern California, Los Angeles, CA, (2) Children's Hospital of LA, Los Angeles, CA, (3) DeepVoxel Inc., Riverside, CA |
| PO-GePV-T-111 | Improvements On Acute Gastrointestinal Toxicity Modeling by Using Deep-Learning Auto-Segmentation R Salazar*, J Duryea, A Leone, S Nair, R Mumme, H Baroudi, T Netherton, E Holliday, T Whitaker, K Hoffman, L Court, J Niedzielski, UT MD Anderson Cancer Center, Houston, TX |
| 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-2 | Automated Contour Edit Tracking to Improve AI Auto-Segmentation S Elguindi*, A Li, M Zhu, L Cervino, H Veeraraghavan, J Jiang, E LoCastro, Memorial Sloan Kettering Cancer Center, New York, NY |
| SU-E-BRB-7 | Pilot Implementation of AI GTVs for SRS to Brain Metastases D Hsu*, M Aristophanous, L Cervino, Y Hu, A Apte, A Iyer, K Beal, B Imber, A Ballangrud, Memorial Sloan Kettering Cancer Center, New York, NY |
| SU-E-BRB-8 | Task-Specific Fine-Tuning for Interactive Deep Learning Segmentation for Lung Fibrosis On CT Post Radiotherapy MJ Trimpl1,2,3*, P Salome4,5,6,7, D Walz4,5,6,7, J Hoerner-rieber6,7, S Regnery6,7, EPJ Stride1, KA Vallis3, J Debus6,7, A Abdollahi4,5,6,7, MJ Gooding2, M Knoll4,5,6,7, (1) Institute of Biomedical Engineering, Department of Engineering Science, Oxford, GB, (2) Mirada Medical, Oxford, GB, (3) Oxford Institute for Radiation Oncology, Oxford, GB, (4) CCU Translational Radiation Oncology, German Cancer Consortium (DKTK) Core-Center Heidelberg, Heidelberg University Hospital (UKHD) and German Cancer Research Center (DKFZ), Heidelberg, DE, (5)Division of Molecular and Translational Radiation Oncology, Heidelberg Faculty of Medicine (MFHD), Heidelberg University Hospital (UKHD), Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg, DE, (6) Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, DE, (7) National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, DE |
| SU-F-201-2 | Improving Cone-Beam CT Auto-Segmentation Accuracy Using Cycle GANs for Domain Adaptation K Shah1*, J Shackleford1, N Kandasamy1, G Sharp2, (1) Drexel University, Philadelphia, PA, (2)Massachusetts General Hospital, Boston, MA |
| SU-F-201-3 | Leveraging the Elliptical Shape of the Uterocervix On Semi-Axial Cross-Sections for Improved Deep-Learning Segmentation On Cone-Beam CT S Mason1*, L Wang2, K Zormpas-Petridis2, M Blackledge2, S Lalondrelle1, H Mcnair1, E Harris2, (1) Royal Marsden NHS Foundation Trust, Sutton, SRY, GB, (2) Institute Of Cancer Research |
| 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-F-BRB-5 | Deep Learning-Based Auto Segmentation Using Generative Adversarial Network On Magnetic Resonance Images for Head and Neck Cancer D Kawahara1*, A Saito2, Y Nagata3, (1) ,Department of Radiation Oncology, Hiroshima, JP |
| SU-F-BRB-6 | Is Public Data Enough? A Comparison of Public and Institutional Deep Learning Models for Segmentation of 17 Organs-At-Risk in the Head and Neck Brett Clark1,2, Nicholas Hardcastle2,3,4, Price Jackson2,4, Leigh Johnston1, James C Korte1,2, (1) Department of Biomedical Engineering, University of Melbourne, Melbourne, Australia (2) Department of Physical Science, Peter MacCallum Cancer Centre, Melbourne, Australia (3) Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia (4) Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne |
| SU-H330-IePD-F6-1 | Improvement of Online Treatment Planning with the MR-Linac Using Fat-Suppressed MRI T Salzillo1*, A Dresner2, A Way1, K Wahid1, S Mulder1, A Yoder1, S Ahmed1, K Corrigan1, G Manzar1, L Andring1, C Pinnix1, R Stafford3, A Mohamed1, J Wang4, C Fuller1, (1) Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX (2) Philips Healthcare (3) Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX (4) Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX |
| SU-H330-IePD-F9-3 | A Framework for Reproducible and Scalable Radiomics Pipelines for Radiation Oncology Using Self-Contained Containers and Workflow Manager W Choi1*, H Nourzadeh1, S Lee2, Y Chen1, N Ghassemi1, Y Vinogradskiy1, A Dicker1, (1) Thomas Jefferson University Hospital, Philadelphia, PA, (2) Marshall University, Huntington, WV |
| SU-H400-IePD-F6-1 | Intracranial Vessel Wall Segmentation Using a Novel Tiered Loss to Capture Class Inclusion H Zhou1*, J Xiao2, Z Fan2, 3, 4, D Ruan1, 5, (1) Department of Bioengineering, University of California, Los Angeles, CA 90095, (2) Department of Radiology, University of Southern California, Los Angeles, CA 90033, (3) Department of Radiation Oncology, University of Southern California, Los Angeles, CA 90033, (4) Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089 (5) Department of Radiation Oncology, University of California, Los Angeles, CA 90095 |
| SU-H400-IePD-F8-1 | Dense UNet for Automatic Contour Correction On Abdominal MRI for MR-Guided Adaptive Radiation Therapy C Sarosiek*, J Ding, A Amjad, Y Zhang, N Dang, X Li, Medical College of Wisconsin, Milwaukee, WI |
| SU-H430-IePD-F5-2 | Automated Brain Metastases Segmentation with a Deep Dive Into False Positives Detection H Ziyaee1*, C Cardenas2, D Yeboa1, j Li1, J Johnson1, Z Zhou1, J Sanders1, R Mumme1, L Court1, T Briere1, J Yang1, (1) UT MD Anderson Cancer Center, Houston, TX, (2) The University of Alabama at Birmingham, Birmingham, AL |
| SU-H430-IePD-F5-3 | A Sensitivity Analysis On the Relationship Between Dose and Overlap Metrics for Head & Neck Normal Tissues B Marquez1,2*, C Owens1,2, K Huang1,2, M El Basha1,2, R Mumme2, C Nguyen2, C Peterson1,2, D Fuentes1,2, T Whitaker1,2, T Netherton1,2, L Court1,2,(1) University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, (2) MD Anderson Cancer Center, Houston, TX |
| SU-H430-IePD-F5-4 | Developing a Head-And-Neck CBCT Segmentation Network From Unlabeled Data Via Domain Adaptation and Self-Training T Mengke, X Liang, H Morgan, H Shao, S Jiang, Y Zhang*, UT Southwestern Medical Center, Dallas, TX |
| 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-206-4 | Segmentation by Test-Time Optimization for CBCT-Based Adaptive Radiation Therapy X Liang*, T Bai, J Chun, H Morgan, D Nguyen, J Park, S Jiang, University of Texas Southwestern Medical Center, Dallas, TX |
| 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 |
| TU-A-202-2 | ASSET: Auto-Segmentation of the Seventeen SEgments for Ventricular Tachycardia Ablation in Radiation Therapy E Morris1*, R Chin1, T Wu1, C Smith1, S Nejad-Davarani2, M Cao1, (1) Department of Radiation Oncology, UCLA Health, Los Angeles, CA, (2) Department of Radiation Oncology, University of Michigan, Ann Arbor, MI |
| TU-D1000-IePD-F5-1 | Impact of Multi-Energy CT On Tumor Delineation of Primary Head and Neck Tumors J Miller1*, L DiMaso-Myers1, P Harari1, B Morris1, E Merfeld1, J Shah2, A Burr1, M Lawless1, (1) University of Wisconsin-Madison, Madison, WI,(6) Siemens Healthineers, Durham, NC |
| TU-D1000-IePD-F7-1 | Deep Learning-Based Automatic Segmentation Framework for Targets and OARs in Cervical High Dose Rate (HDR) Brachytherapy R Ni1*, S Kim1, B Haibe-kains1, A Rink1,2, (1) University of Toronto, Toronto, ON, CA, (2) Princess Margaret Hospital, Toronto, ON, CA |
| TU-D1030-IePD-F2-1 | An Initial Longitudinal Performance Analysis for a Deep Learning-Based Medical Image Segmentation Model B Wang1,2*, M Dohopolski1, T Bai1, M Lin1, J Wu1, D Nguyen1, S Jiang1, (1) UT Southwestern Medical Center, Dallas, TX, (2) Southern Methodist University, Dallas, TX |
| TU-D1030-IePD-F2-2 | Deep Learning Method for Objective Segmentation of Radiation-Induced Dermatitis in Skin Photographs Y Park1*, S Choi2, M Cho2, J Son2, M Han1, J Kim3, H Kim1, D Kim4, J Kim1, C Hong1, (1) Yonsei University College of Medicine, Seoul, , KR, (2) Yongin Severance Hospital, Yongin, ,KR, (3) Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul, Seoul, KR, (4) Yonsei Cancer Center, Seoul, 11, KR, |
| TU-D1030-IePD-F2-3 | Segmentation of the Left Anterior Descending Coronary Artery Using Curvature-Guided Deep Learning for Lung Cancer Patients Treated with Radiotherapy Y Yue*, A Curtis, S Ng, K Huang, T Lautenschlaeger, Indiana University- School of Medicine, Indianapolis, IN |
| TU-D1030-IePD-F2-4 | Deep Learning Based Automatic Contour Quality Assurance for Auto-Segmentation of Abdominal Organ N Dang*, Y Zhang, A Amjad, J Ding, C Sarosiek, X Li, Medical College of Wisconsin, Milwaukee, WI |
| TU-D1030-IePD-F2-5 | An Independent Evaluation of Six Commercially Available Deep Learning-Based Auto Segmentation Platforms Using Large Multi- Institutional Datasets L Yuan1*, Q Chen2, Y Rong3, H Al-Hallaq4, S Benedict5, B Cai6, Q Wu7, K Latifi8, Y Xiao9, X Yang10, X Qi11, (1) Virginia Commonwealth University Medical Center, Richmond, Virginia, (2) City of Hope Medical Center, Duarte, CA, (3) Mayo Clinic Arizona, Phoenix, AZ, (4) The University of Chicago, Chicago, IL, (5) UC Davis Cancer Center, Davis, CA, (6) University of Texas Southwestern Medical Center, Clayton, MO, (7) Duke University Medical Center, Chapel Hill, NC, (8) H. Lee Moffitt Cancer Center, Tampa, FL, (9) University of Pennsylvania, Philadelphia, PA, (10) Emory University, Atlanta, GA, (11) UCLA School of Medicine, Los Angeles, CA |
| TU-D930-IePD-F9-2 | Weakly Supervised Convolutional Neural Networks for Segmentation of Diffusely Abnormal White Matter in Multiple Sclerosis B Musall1*, A Kamali1, J Lincoln1, V Ly2, X Luo2, P Narayana1, R Gabr1, (1) University of Texas McGovern Medical School, Houston, TX, (2) University of Texas School of Public Health, Houston, TX |
| TU-GH-BRC-0 | Brain Mets Management: Registries and Automation for Longitudinal Assessment K Bernstein1*, W Shi2*, D Schlesinger3*, H Lin4*, X Gu5*, T Li6*, (1) NYU Langone Medical Center, New York, NY, (2) Thomas Jefferson University, Philadelphia, PA, (3) University of Virginia Health Systems, Charlottesville, VA, (4) University of California San Francisco, San Francisco, CA, (5) Stanford University, Dallas, TX, (6) University of Pennsylvania, Philadelphia, PA |
| TU-H-BRA-2 | A User-Specific Automatic Contouring, Planning, and QA Solution for Cervical Cancer Radiotherapy D Rhee1*, A Jhingran1, B Rigaud1, K Huang1, C Anakwenze1, K Kisling2, B Beadle3, C Cardenas4, S Kry1, S Prajapati1, L Zhang1, K Brock1, W Shaw5, H Simonds6, L Court1, (1) MD Anderson Cancer Center, Houston, TX, (2) UC San Diego, La Jolla, CA, (3) Stanford University, Stanford, CA, (4) The University of Alabama at Birmingham, Birmingham, AL, (5) University of the Free State, Bloemfontein, ZA, (6) Stellenbosch University, Stellenbosch, Cape Town, ZA |
| TU-I345-IePD-F3-3 | Atlas Based Automatic Segmentation, A Clinical Implementation & Assessment of Accuracy S Costello*, D Pearson, University of Toledo, Toledo, OH |
| TU-J430-BReP-F2-4 | Dominant Index Prostatic Lesions Segmentation Using Deep Learning for MR-Guided Radiative Ablation J Simeth*, J Jiang, A Nosov, A Wimber, M Zelefsky, N Tyagi, H Veeraraghavan, MSKCC, New York, NY |
| 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-B-201-3 | Fully Automated Segmentation of Prostatic Urethra for MR-Guided Radiation Therapy (MRgRT) D XU1,2*, T Ma2, R Savjani2, M Cao2, Y Yang2, A Kishan2, F Scalzo3, K Sheng2, (1)Computer Science, University of California, Los Angeles, CA 90095, USA (2) Radiation Oncology, University of California, Los Angeles, CA 90095, USA (3)Computer Science, Pepperdine University, 24255 Pacific Coast Hwy, Los Angeles, CA 90263, USA |
| WE-C1000-IePD-F6-1 | Active Bone Marrow Delineation in a Swine Model Via Proton Density Fat Fraction MRI and Multi-Energy CT D Coleman1*, J Meudt1, D Shanmuganayagam1, J Shah2, A McMillan1, A Pirasteh1, J Miller1, B Bednarz1, M Lawless1, (1) University of Wisconsin, Madison, WI (2) Siemens Healthineers, Durham, NC |
| WE-C1030-IePD-F2-1 | Deep-Learning Method for Segmenting Head and Neck Tumours in KV Images Acquired During Radiotherapy M Gardner1*, A Mylonas1, M Mueller1, Y Ben Bouchta1, J Sykes2, P Keall1, D Nguyen7, (1) University of Sydney, Sydney, NSW, AU (2) Blacktown Hospital, Blacktown, AU,(3) University of Technology Sydney, Ultimo, NSW |
| WE-C1030-IePD-F2-2 | Transformer-Based Deep Learning Architecture for Improved Cardiac Substructure Segmentation N Summerfield1, 2*, J Qiu3, S Hossain1, M Dong3, C Glide-Hurst1, 2, (1) University of Wisconsin Madison, Department of Human Oncology, (2) University of Wisconsin Madison, Department of Medical Physics, (3) Wayne State University, MI, Department of Computer Science |
| WE-E-202-6 | Variations in Activity and Dose Calculations in Pre-Treatment Planning for Hepatic Lobar Y-90 Microspheres Therapy When Using Anatomic Versus Functional Imaging Contouring Techniques D Alvarez1*, L Rodriguez1, R Herrera1, T Kutuk1, E Saugar1, A Kaiser1, M Chuong1, A Gutierrez1, S Kim1, R Gandhi1,2, (1) Miami Cancer Institute, Miami, FL, (2) Miami Cardiac & Vascular Institute, Miami, FL |
| WE-G-BRC-4 | Evaluation of Commercial AI Segmentation Software J Roper*, T Wang, Y Lei, S Dresser, B Ghavidel, L Qiu, J Zhou, O Kayode, K Luca, J Bradley, T Liu, X Yang, Emory University, Atlanta, GA |
| 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 |