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Taxonomy: IM/TH- Image Segmentation Techniques: Machine Learning
| 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-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 |
| 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-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-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-212 | Application of a Convolutional Neural Network to the Segmentation of Lungs in Mice E Criscuolo1*, R Sali2, E Graves3, L Soto4, (1) University of Connecticut, Storrs, CT, (2) Stanford University, Stanford, CA ,(3) Stanford University, Stanford, CA, (4) Stanford University, Stanford, CA, |
| 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-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 |
| 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-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-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-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 |
| 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 |
| 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-I345-IePD-F6-6 | To Evaluate the Clinical Implementation Feasibility of the Siemen's Auto-Contouring Deep-Learning Solution, AI-Rad Companion Organs RT L Maduro Bustos1,2*, L Doyle1, D Nurbagandova1, J Noonan1, A Sarkar1, K Andreou1, P Graner3, K Saddi3, F Mourtada1,2, (1) ChristianaCare, Newark, DE, (2) Thomas Jefferson University, Philadelphia, PA, (3) Siemens Healthineers, Malvern, PA. |
| TU-J430-BReP-F2-1 | Incrementally Re-Trained AI-Models for Organ and Target Auto-Segmentation for Post-Prostatectomy Patients D Hobbis1*, K Mund1, Q Chen2, X Feng3,4, N Yu1, C Vargas1, S Schild1, J Rwigema1, S Keole1, W Wong1, Y Rong1, (1) Mayo Clinic, Phoenix, AZ, (2) University of Kentucky, Lexington, KY, (3) University of Virginia, Charlottesville, VA, (4) Carina Medical LLC, Lexington, KY |