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Taxonomy: IM/TH- Image Segmentation Techniques: Machine Learning
| PO-GePV-M-1 | Clinical Evaluation of Deep Learning Based Auto-Contouring Software for Prostate Radiotherapy M Kirk1*, B Anderson1, H Prichard1, L Ryan1, X Zhang1, Y Wang2, (1) Mass General/North Shore, Danvers, MA,(2) Massachusetts General Hospital, Boston, MA |
| PO-GePV-M-2 | Comparison of Deep Learning Based Auto-Contouring Software to Expert Inter-Observer Variability in Prostate Radiotherapy M Kirk1*, B Anderson1, H Prichard1, L Ryan1, X Zhang1, Y Wang2, (1) Mass General/North Shore, Danvers, MA, (2) Massachusetts General Hospital, Boston, MA |
| PO-GePV-M-38 | What's Behind Auto-Segmentation Models: An Interpretability Analysis of Calculation Logic of Segmentation Model for Brain Tumor H Chen1*, D Ban2, X Qi3, (1) Xi'an University of Posts and telecommunications, Xi'an, 61, CN, (2) Xi'an University of Posts and telecommunications, Xi'an, 61, CN, (3) UCLA School of Medicine, Los Angeles, CA |
| PO-GePV-M-216 | Head and Neck Multi-Organ Segmentation On Dual-Energy CT Using Dual Pyramid Convolutional Neural Networks T Wang*, Y Lei, J Roper, B Ghavidel, J Beitler, M McDonald, J Bradley, T Liu, X Yang, Emory Univ, Atlanta, GA |
| PO-GePV-M-238 | Using Deep Learning Auto-Planning for Evaluating the Dosimetric Impact of Deep Learning Auto-Segmentation Without Human Intervention Over Multiple Dose Escalation Schemes for Lung SBRT Treatment Planning M Holmstrom1*, E Samuelsson1, Y Wang2, (1) RaySearch Laboratories, Stockholm, Sweden, (2) Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA |
| SU-B-TRACK 6-3 | Evaluating the Clinical Acceptability of Deep Learning Contours in Prostate Radiotherapy J Duan*, M Bernard, B Willows, L Downes, W Mourad, W St Clair, X Feng, Q Chen, University of Kentucky, Lexington, KY |
| SU-IePD-TRACK 3-3 | Segmentation of a Highly Deformable Organ Using a Machine-Assisted Interpolation Algorithm D Luximon*, Y Abdulkadir, E Morris, P Chow, J Lamb, University of California, Los Angeles, Los Angeles, CA |
| TH-D-TRACK 3-2 | Automated Tumor Localization and Segmentation Through Hybrid Neural Network in Head & Neck Cancer A Qasem1*, Z Zhou2, (1) ,Warrensburg, MO, (2) University of Central Missouri, Warrensburg, MISSOURI |
| TH-D-TRACK 3-5 | Unsupervised COVID-19 Pneumonia Lesion Segmentation in CT Scans Using Cycle Consistent Generative Adversarial Network Y Liu*, C Fang*, J Wen, Y Yang, University of Science and Technology of China, Hefei, Anhui, P. R. China * The authors contribute equally |
| TH-E-TRACK 4-2 | Multi-Year Clinical Experience with In-House Developed AI Auto-Segmentation for Radiotherapy Planning S Elguindi1*, J Jiang1, A Apte1, A Iyer1, E LoCastro1, Y Hu1, E Cha2, E Gillespie2, I Onochie2, D Gorovets2, M Zelefsky2, S Berry1, M Thor1, J Deasy1, L Cervino1, H Veeraraghavan1, (1) Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States (2) Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States |
| TU-E-TRACK 6-2 | Cardiac Substructure Segmentation Using a Mask-Scoring Attention Convolutional Neural Network J Harms*, Y Lei, S Tian, N Mccall, K Higgins, J Bradley, T Liu, X Yang, Winship Cancer Institute of Emory University, Atlanta, GA |
| TU-E-TRACK 6-3 | GAN-Driven Anomaly Detection for Active Learning in Medical Imaging Segmentation M Woodland*1,2, A Patel2,3, B Anderson1, E Lin1, E Koay1, B Odisio1, K Brock1, (1) The University of Texas MD Anderson Cancer Center, Houston, TX, (2) Rice University, Houston, TX, (3) Baylor College of Medicine, Houston, TX |
| TU-F-TRACK 6-3 | Autosegmentation Anomaly Detection by Clustering of Shapes D Sawkey*, Varian Medical Systems, Toronto, Canada |
| TU-IePD-TRACK 4-1 | A Deep Reinforcement Learning Based Pipeline for Prostate Segmentation On MRI with Low Variance Performance L Xu1*, W Shi1, N Wen2, (1) Wayne State University, Detroit, MI, (2) Henry Ford Health System, Detroit, MI |
| TU-IePD-TRACK 4-5 | On the Application of a Variational Autoencoder (VAE) and Transfer Learning to Account for Inter-Observer Uncertainties in Automatic Prostate Gland Segmentation H Bagher-Ebadian*1,2, X Li3, E Mohamed1,2, B Movsas1,2, D Zhu3, IJ Chetty1,2, (1) Henry Ford Health System, Detroit, MI, (2) Henry Ford Cancer Institute, Detroit, MI, (3) Wayne State University, Detroit, MI |