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Taxonomy: IM- MRI : Machine learning, computer vision
|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
|PO-GePV-M-57||Prostate Cancer Malignancy Detection and Localization From MpMRI Using Auto-Deep Learning: One Step Closer to Clinical Utilization|
W Zong1, E Carver2*, S Zhu3, E Schaff4, D Chapman5, J Lee6, I Chetty7, W Wen8, (1) Henry Ford Health System, Troy, MI, (2) Wayne State University, Troy, MI, (3) ,,,(4) Henry Ford Health System, ,,(5) Henry Ford Health System, ,,(6) Trinity Health, ,,(7) Henry Ford Health System, Detroit, MI, (8) Henry Ford Health System,
|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
|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
|TH-F-BRC-3||BEST IN PHYSICS (MULTI-DISCIPLINARY): Motion-Corrected Image Reconstruction with Unrolling Networks On An MRI-Linac|
S Shan1,2, Y Gao3, P Liu1,2, T Reynolds1*, B Dong2, H Sun3, M Li3, G Liney2, F Liu3, P Keall1,2, D Waddington1,2, (1) ACRF ImageX Institute, University of Sydney, Sydney, NSW 2015, Australia (2) Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia(3)School of Information Technology & Electrical Engineering, University of Queensland, Brisbane, QLD 4067, Australia
|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-J430-BReP-F2-5||Implementation of Super-Resolution Imaging On An MRI-Linac|
J Grover1, P Liu1,2, B Dong2, S Shan1,2, B Whelan1,2, P Keall1,2*, D Waddington1,2, (1) ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, NSW, AU, (2) Department of Medical Physics, Ingham Institute for Applied Medical Research, NSW, AU.
|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