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A B C D E F G H I J K L M N O P Q R S T U V W X Y Z # | show all

Keywords: segmentation
BReP-SNAP-M-12A Novel Multi-Path 3D Dense-UNet for Improving Automatic Segmentation of the Glioblastoma GTV On Multi-Modal MR Images
J Lewis1*, K Singhrao2, Y Yang2, X Qi2, D Ruan2, J Fu2, (1) Cedars-Sinai Medical Center, Beverly Hills, CA, (2) Department of Radiation Oncology, UCLA, Los Angeles, CA
BReP-SNAP-M-21Automatic CT Segmentation for Radiotherapy Treatment Planning: How Good Is Good Enough?
W S Ingram*, L Dong, University of Pennsylvania, Philadelphia, PA
BReP-SNAP-M-25Automatic Tumor and Multi-Organ Segmentation Technique in CT Based On Deep Learning for Radiation Therapy After Breast-Conserving Surgery
J Lee1*, H Cho1*, S Ye1, D Choi2, W Park2, H Kim2, W Cho2, H Kim2, (1) Seoul National University, Seoul, 41, KR, (2) Samsung Medical Center, Seoul, 41, KR (*J Lee and H Cho contributed equally to this work.)
BReP-SNAP-M-26Auto-Segmentation of Pelvic OARs On MRI Multi-Sequence Using An Fused-Unet
Zesen Cheng1,2, Tianyu Zeng1,2, Yimei Liu1, Lijuan Lai2, Xin Yang1*, Sijuan Huang1, (1) Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, (2) Department of Electronic and Information Engineering, South China University of Technology, Guangzhou, Guangdong, 510641, China
BReP-SNAP-M-28Beams-Eye-View Tracking of Prostate Fiducial Markers During VMAT Treatments
A Mylonas1,2*, E Hewson1, P Keall1, J Booth3, D Nguyen1,2, (1) ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia, (2) School of Biomedical Engineering, University of Technology Sydney, Sydney, NSW, Australia, (3) Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, Australia
BReP-SNAP-M-29Branch-UNet for Intraprostatic Lesion Segmentation in Multi-Parametric MRI Images for Boosting Radiotherapy of Prostate Cancer
Y Chen*, L Xing, H Bagshaw, M Buyyounouski, B Han, Stanford Univ School of Medicine, Stanford, CA
BReP-SNAP-M-36Classification and Segmentation of Tumor Cells and Nuclei On Biopsy Slides Using Deep Learning for Microdosimetry Applications
L Weishaupt*, J Torres, S Camilleri-Broët, S Côté Maldonado, S Abbasinejad Enger, McGill University, Montreal, QC, CA
BReP-SNAP-M-45CT-Based Convolutional-Neural-Network Segmentation of HCC Regions with Lung-Cancer-Based Transfer Learning
N Nagami12*, H Arimura2, J Nojiri3, R Nakano2, K Ninomiya2, M Ogata1, S Takita1, S Kitamura1, H Irie3, (1) Saga university hospital, Saga-shi, Saga, JP, (2) Kyushu University, Fukuoka, Fukuoka, JP, (3) Saga University, Saga-shi, Saga, JP,
BReP-SNAP-M-58Development and Implementation of a Knowledge Base for Automated Segment Review
E Pryser*, M Schmidt, F Reynoso, W Smith, Washington University in St. Louis, St. Louis, MO
BReP-SNAP-M-91Incorporating GTV Information in a Multi-Stage Process to Improve Automatically Generated Field Apertures for Rectal Cancer Radiotherapy
K Huang*, P Das, L Zhang, M Amirmazaheri, C Nguyen, D Rhee, T Netherton, S Beddar, T Briere, D Fuentes, E Holliday, L Court, C Cardenas, MD Anderson Cancer Center, Houston, TX
BReP-SNAP-M-97Lifelong Learning for Clinical Target Segmentation of Nasopharyngeal Cancer with Fewer Labeling
K Men*, X Chen, Y Zhang, J Zhu, J Yi, J Dai, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, 100021, China,
BReP-SNAP-M-112Plan Quality-Driven Evaluation of Automated Segmentation for Radiotherapy
J Zhu, X Chen, T Zhang, N Bi, K Men, J Dai*, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, China,
BReP-SNAP-M-134Segmentation of Invisible Target Volume with Estimated Uncertainties for Post-Operative Prostate Cancer Radiotherapy
A Balagopal*, D Nguyen, M Lin, H Morgan, N Desai, R Hannan, A Garant, Y Gonzalez, A Sadeghnejad Barkousaraie, S Jiang, UT Southwestern Medical Center, Dallas, TX
MO-CD-TRACK 1-5RmU-Net: A Generalizable Deep Learning Approach for Automatic Prostate Segmentation in 3D Ultrasound Images
N Orlando1,2*, D Gillies1,2, I Gyackov2, C Romagnoli3,5, D D'Souza4,5, A Fenster1-4, (1) Department of Medical Biophysics, Western University, London, ON, CA, (2) Robarts Research Institute, Western University, London, ON, CA, (3) Department of Medical Imaging, Western University, London, ON, CA, (4) Department of Oncology, Western University, London, ON, CA, (5) London Health Sciences Centre, London, ON, CA
MO-F-TRACK 2-2A Deep Learning Based Segmentation and Evaluation Framework for Brain Metastases Follow-Up After Stereotactic Radiosurgery
Z Yang1*, L Wang2, Y Liu3, M Chen1, E Zhang1, R Timmerman1, T Dan1, Z Wardak1, W Lu1, X Gu1, (1) UT Southwestern Medical Center, Dallas, TX (2) University Of Texas At Arlington, TX (3) Sichuan University, Chengdu, CN
MO-F-TRACK 2-4OARnet: Organs-At-Risk Delineation in Head and Neck CT Images
M H Soomro*, H Nourzadeh, V Leandro Alves, W Choi, J Siebers, University of Virginia, Charlottesville, VA
MO-F-TRACK 2-6Multi-Modality Convolutional Neural Network for Automatic Lung Tumor Segmentation
S Wang*, L Yuan, R Mahon, E Weiss, Virginia Commonwealth University, Richmond, VA
PO-GeP-I-9A Machine Learning Based Automatic Lung Lobe Segmentation in Fast Helical Free Breathing CT Scans
L Naumann*, B Stiehl, M Lauria, R Pande, S Suthar, H Sundaram, S Narayanan, S Siva, D Low, A Santhanam, UCLA, Los Angeles, CA
PO-GeP-I-19A Semi-Automatic Cardiac Substructure Segmentation Platform for Radiotherapy Planning CT
E Zhang1*, J Li2, R Timmerman3, P Alluri4 , M Chen5, W Lu6, X Gu7, (1) The University of Texas Southwestern Medical Ctr, Dallas, TX, (2) Guangdong General Hospital, Guangzhou, CN, (3) UT Southwestern Medical Center, Dallas, TX, (4) UT Southwestern Medical Center, Dallas, TX, (5) UT Southwestern Medical Center, Dallas, TX, (6) UT Southwestern Medical Center, Dallas, TX, (7) UT Southwestern Medical Center, Dallas, TX
PO-GeP-I-66Cross-Modality Esophagus Segmentation Using Physics-Based Data Augmentation
S Alam1*, T Li2, S Zhang3, D Lee4, P Zhang5, S Nadeem6, (1), (2), (4), (5), (6) Memorial Sloan Kettering Cancer Center, New York, NY, (3) Peking University Cancer Hospital & Institute, Beijing, China
PO-GeP-I-122Feasibility of Anatomy-Specific Exposure Index in Clinical Digital Radiography Images
Z Long*, L Littrell, B Schueler, Mayo Clinic, Rochester, MN
PO-GeP-I-135Impact of a 3D Convolution Neural Network Method On Liver Segmentation: An Accuracy and Time-Savings Evaluation
NM Cole1*, H Wan1, J Niedbala2, YK Dewaraja3, A Kruzer1, D Pittock1, C Halley1, AS Nelson1, (1) MIM Software Inc., Cleveland, OH, (2) Michigan Medicine, Ann Arbor, MI, (3) University of Michigan, Ann Arbor, MI
PO-GeP-I-199Study On the Influence of Lung Cancer Target Delineation with Different SUV Thresholds On Feature Extraction in 18F-FDG-PET Images
C Ma*, Shandong Cancer Hospital and Institute,Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, N/ACN,
PO-GeP-I-219Training and Validation of a Commercial Deep Learning Contouring Platforms
J Koo*, J Caudell, V Feygelman, E Moros, K Latifi, H. Lee Moffitt Cancer Center, Tampa, FL
PO-GeP-M-10A Deep Learning-Based Interactive Software Tool to Assist Physicians Revising CTV Contours to Achieve Balanced Tumor Coverage and Organ Sparing
A Balagopal*, D Nguyen, m mashayekhi, M Lin, A Garant, N Desai, R Hannan, Y Weng, X Gu, S Jiang, UT Southwestern Medical Center, Dallas, TX
PO-GeP-M-17A Hierarchical 3D U-Net for Brain Tumor Substructure Segmentation
J Yang1, R Wang1,2,3, Y Weng2,3*, L Chen2,3, Z Zhou4, (1) School of Artificial Intelligence, Xidian University, Xi'an, CN. (2) UT Southwestern Medical Center, Dallas, TX. (3) Medical Artificial Intelligence and Automation (MAIA) Lab, University of Texas Southwestern Medical Center, Dallas, TX. (4) University Of Central Missouri, Warrensburg, MO.
PO-GeP-M-21A Method to Improve Organ Segmentation Between Medical Centers Using a Small Amount of Training Data
K Men1, J Zhu1, X Chen1, Y Yang2, J Zhang2, J Yi1, M Chen2, J Dai1*, 1.National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Beijing,China 2.Zhejiang Cancer Hospital, University of Chinese Academy of Sciences, Hanzhou, China
PO-GeP-M-30A Novel Semi-Supervised Learning Method Using Soft-Label for Lung Segmentation On CT
J Zhou1*, Z Yan2, Y Zhang1, N Yue1, (1) Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, (2) SenseBrain Technology Limited LLC, Princeton, NJ
PO-GeP-M-52An AI-Based Tumor Auto-Contouring Algorithm for Non-Invasive Intra-Fractional Tumor-Tracked Radiotherapy (nifteRT) On Linac-MR
J Yun1*, E Yip2, Z Gabos3, N Usmani4, D Yee5, K Wachowicz6, B Fallone7, (1) (2) (6) (7) Department of Medical Physics, Cross Cancer Institute, Edmonton, AB, CA, (3) (4) (5) Department of Radiation Oncology, Cross Cancer Institute, Edmonton, AB, CA
PO-GeP-M-58An Optimized Training Module for Deep Learning-Based Auto-Segmentation
A Amjad1*, W Xhang2, Z Chen2, Q Zhou2, T Plautz1, L Buchanan1, X A Li1, (1) Medical College of Wisconsin, Milwaukee, WI, (2) Manteia Medical Technologies, Brookfield, WI
PO-GeP-M-79Automatic Prostate Bed Target Segmentation On Daily Cone-Beam CT Image Using a Multi-Path 3D Dense-UNet
J Fu1*, S Yoon1, A Kishan1, K Singhrao1, Z Wang1, J Lewis2, D Ruan1, (1) Department Of Radiation Oncology, UCLA, Los Angeles, CA, (2) Cedars-Sinai Medical Center, Beverly Hills, CA.
PO-GeP-M-80Automatic Segmentation of Prostate Bed in Post-Prostatectomy CT Images
X Xu*, J Lian, Univ North Carolina, Chapel Hill, NC
PO-GeP-M-81Automatic Segmentation of the Prostate On CT Images Using a Bi-Directional Convolutional LSTM U-Net with Novel Loss Function
X Li1*, H Bagher-Ebadian2, C Li1, E Mohamed2, F Siddiqui2, B Movsas2, D Zhu1, I Chetty2, (1) Wayne State University (2) Henry Ford Health System, Detroit, MI
PO-GeP-M-82Automatic Segmentation of the Trigeminal Nerve On MRI Using Deep Learning
K Mulford*, S Ndoro, S Moen, Y Watanabe, PF Van De Moortele, University of Minnesota, Minneapolis, MN
PO-GeP-M-83Automatic Tumor Segmentation in Digital Breast Tomosynthesis Using U-Net
A Qasem1*, G Qin2, J Wang3, Z Zhou4, (1) University of Central Missouri, Warrensburg, MO, (2) Southern Medical University, Guangzhou, ,CN, (3) UT Southwestern Medical Center, Dallas, TX, (4) University Of Central Missouri, Warrensburg, MO
PO-GeP-M-84Auto-Segmentation On Liver With U-Net And Pixel Deconvolutional U-Net
H Yao*, J Chang, Northwell Health and Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Lake Success, NY
PO-GeP-M-98Clinical Evaluation of Atlas and Deep Learning-Based Automatic Contouring of Multiple Organs at Risk and Clinical Target Volumes for Breast Cancer
M Choi1*, B Choi2, S Chung3, N Kim4, J Chun5, Y Kim6, J Chang7, J Kim8, (1) Department of Radiation Oncology, Yonsei Cancer Center, Seoul, KR, (2) Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, KR, (3) Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, KR, (4) Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, KR, (5) Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, KR, (6) Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, KR, (7) Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, KR, (8) Department of Radiation Oncology, Yonsei Cancer Center, Seoul, KR
PO-GeP-M-99Clinical Evaluation of Deep Learning and Atlas Based Auto-Contouring of Bladder and Rectum for Prostate Radiotherapy
J Zabel1,2, J Conway2,3, A Gladwish2,3, J Skliarenko2,3, G Didiodato1,2, L Goorts-matthews2, A Michalak2, S Reistetter2, J King2, K Malkoske2, K Nakonechny2, M Tran2, N McVicar2*, (1) McMaster University, Hamilton, ON, Canada, (2) Simcoe Muskoka Regional Cancer Program, Barrie, ON, Canada, (3) Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
PO-GeP-M-118Completely Automatic Deformable Image Registration Pipeline Using Automatically Derived Segmentations and Deformation Vector Fields
E McKenzie1*, N Tong1, R Neph1, Y Jia2, D Ruan3, K Sheng3, (1) UCLA, Los Angeles, CA, (2) Shaanxi Key Laboratory of Network Data Intelligent Processing, Xi'an, Shaanxi, CN, (3) UCLA School of Medicine, Los Angeles
PO-GeP-M-135Deep Learning for 3D Automated Delineation of Primary Gross Tumor Volume for Nasopharyngeal Carcinoma by CT Combining Contrast-Enhanced CT
Z Dai1*, X Wang2, H Jin3, C Cai4, S Zhao5, Y Zhu6, Y Chen7, (1) The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, CN, (2) The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, CN, (3) The Second Affiliated Hospital Of Guangzhou University Of Chinese Medicine, Guangzhou, Guangdong, CN, (4) The Second Affiliated Hospital Of Guangzhou University Of Chinese Medicine, Guangzhou, Guangdong, AF, (5) The Second Affiliated Hospital Of Guangzhou University Of Chinese Medicine, Guangzhou, Guangdong, CN, (6) The Second Affiliated Hospital Of Guangzhou University Of Chinese Medicine, Guangzhou, Guangdong, CN, (7) The Second Affiliated Hospital Of Guangzhou University Of Chinese Medicine, Guangzhou, Guangdong, CN
PO-GeP-M-137Deep Learning Segmentation of Cardiac Substructures in Breast Cancer Radiotherapy Patients
X Jin*, J Hilliard, J Dise, J Kavanaugh, I Zoberi, M Thomas, C Robinson, G Hugo, Washington University School of Medicine, St. Louis, MO
PO-GeP-M-138Deep Learning-Based Auto-Segmentation of OARs in Head and Neck CT Images
Z Shen1*, A Garsa1, S Sun2, N Bai2, C Zhang2, A Shiu1, E Chang1, W Yang1, (1) University of Southern California, Los Angeles, CA, (2) DeepVoxel Inc., Irvine, CA
PO-GeP-M-177Dosimetric Variation Between Manual Contouring and Auto-Segmentation for Normal Structures in Intensity Modulated Radiotherapy
C Alekchander1*, V Kaliyaperumal2, S Chawla3, A Agarwal4, S Goel5, A Sharma6, (1) Patel Hospital Pvt. Ltd, Jalandhar, PB, IN, (2) Medanta The Medicity, Gurgaon, HR, IN, (3) Patel Hospital Pvt Ltd, ,,(4) ,,,(5) Patel Hospital, ,,(6) Patel Hospital Pvt Limited,
PO-GeP-M-193Evaluating the Accuracy of Atlas-Based Auto-Segmentation for Pediatric Craniospinal Irradiation
S Al-ward*, O Ates, M Gargone, T E. Merchant, L Zhao, St. Jude Children's Research Hospital, Memphis, TN
PO-GeP-M-199Evaluation of Deep Learning-Based Auto-Segmentation of Target Volume and Normal Organs in Breast Cancer Patients
SY Chung1*, JS Chang1, Y Chang2, BS Choi1, J Chun1, JS Kim1, YB Kim1, (1) Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, KR, (2) CorelineSoft, Co., Ltd, KR
PO-GeP-M-252Improved Auto-Segmentation for CT Male Pelvis: Comparison of Deep Learning to Traditional Atlas Segmentation Methods
C Halley*, H Wan, A Kruzer, D Pittock, D Darkow, M Butler, N Cole, M Bending, P Jacobs, AS Nelson, MIM Software Inc., Cleveland, OH
PO-GeP-M-261Initial Experience in MRI-Based Brain Metastases Detection Using Deep Learning
J Teruel1*, K Bernstein1, P Galavis1, K Spuhler1, J Silverman1, D Kondziolka1,2, K Osterman1, (1) Department of Radiation Oncology, NYU Langone Health, New York, NY, (2) Center for Advanced Radiosurgery, NYU Langone Health, New York, NY
PO-GeP-M-263Interactive Contouring Through Contextual Deep Learning
M Trimpl1,2,3*, D Boukerroui1, E Stride2, K Vallis3, M Gooding1, (1) Mirada Medical Ltd, New Barclay House, 234 Botley Rd, Oxford OX2 0HP, GB (2) Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus OX37DQ, GB (3) Oxford Institute for Radiation Oncology, University of Oxford, Old Road Campus OX37DQ, GB
PO-GeP-M-280Learned Delineation of Gross Tumor Volume Incorporating Intra-Observer Variability
T Marin*, C Ma, R Lahoud, F Xing, P Wohlfahrt, M Moteabbed, J Woo, X Ma, K Grogg, Y Chen, G El Fakhri, Massachusetts General Hospital, Boston, MA
PO-GeP-M-304Multi-Modality Brain Tumor Segmentation Using a Modified Cascaded 3D U-Net for Imbalanced Classes
W Kong1*, D Li1, YM Yang2, (1) Shandong Normal University, Jinan, 37, CN, (2) UCLA, Los Angeles, CA
PO-GeP-M-307Multiple Resolution Residual Network for Automatic Glioblastoma Segmentation in MRI
H Um*, J Jiang, F Tixier, R Young, H Veeraraghavan, Memorial Sloan Kettering Cancer Center, New York, NY
PO-GeP-M-308Multiple Resolution Residual Network for Automatic Thoracic Organs-At-Risk Segmentation in CT
H Um*, J Jiang, M Thor, A Rimner, L Luo, J Deasy, H Veeraraghavan, Memorial Sloan Kettering Cancer Center, New York, NY
PO-GeP-M-310Normal Tissue and Tumor Segmentation Using V-Net Regularized by YOLO
C Hsu1*, C Morin1, T Kirby2, M Metzger1, J Flerlage1, S Kaste1, M Krasin1, B Shulkin1, J Lucas1, (1) St. Jude Children's Research Hospital, Memphis, TN, (2) Rhodes College, Memphis , TN
PO-GeP-M-370Robustness Study of Deep Learning Based Medical Image Segmentation to Noisy Annotation
S Yu*, E Zhang, J Wu, H YU, L Ma, Z Yang, M Chen, X Gu, W Lu, UT Southwestern Medical Center, Dallas, TX
PO-GeP-M-373Scoring Contour Agreement Using a Beta Distribution Model
T Lim*, X Wang, J Yang, MD Anderson Cancer Center, Houston, TX
PO-GeP-M-374Segmentation Accuracy and Radiomics Feature Stability of Multiple U-Net Based Automatic Segmentations On Ultrasound Images for Patients with Ovarian Cancer
X Jin1*, J Jin2, C Xie3, (1) Wenzhou Medical University First Hospital, Wenzhou, ,CN, (2) ,,,(3) ,Wenzhou, ,CN
PO-GeP-M-386Study of Segmental Spinal Cord Contour Expansion Margin for Esophageal Cancer Patient Under Radiation Treatment
L Dingjie1*, S Wei2, C Yang3, H Ge4, (1) Henan Cancer Hospital, Henan, ,CN, (2) Radiation Oncology Department Of Henan Cancer Hospital, ,,(3) Manteia Medical Technologies, Co., Milwaukee, WI, (4) Radiation Oncology Department Of Henan Cancer Hospital,
PO-GeP-M-417Using Very Small Contour Sets to Train High-Quality Deep-Learning Segmentation Models
Y Zhao*, D Rhee, C Cardenas, L Court, J Yang, The University of Texas MD Anderson Cancer Center, Houston, TX
PO-GeP-M-419Validation and Clinical Application of DL-Based Automatic Target and OAR Segmentation Software, DeepViewer
Z Peng1*, Y Chang2, Y Song3, H Wu4, A Wu5, X Pei6, X Xu7, (1) University of Science and Technology of China, Hefei, (2) University of Science and Technology of China, Hefei, (3) University of Science and Technology of China, Hefei, (4) Anhui Wisdom Technology Company Limited, Hefei, (5) The First Affiliated Hospital of University of Science and Technology of China, Hefei, (6) University of Science and Technology of China, Hefei, (7) Rensselaer Polytechnic Institute, Troy, NY
PO-GeP-M-439Comparison of a 3D Convolutional Neural Network Segmentation Method to Traditional Atlas Segmentation for CT Head and Neck Contours
A Kruzer*, H Wan, M Bending, C Halley, D Darkow, D Pittock, N Cole, P Jacobs, AS Nelson, MIM Software Inc., Cleveland, OH
PO-GeP-T-682Quantitative Versus Qualitative and Dosimetric Evaluation of Automated Segmentations
J Pursley*, G Maquilan, G Sharp, Massachusetts General Hospital and Harvard Medical School, Boston, MA
PO-GeP-T-700Reduction of Interobserver Contouring Variations Through a Retrospective, Evidence-Based Intervention
H Patrick1*, L Souhami2, J Kildea1,2, (1) McGill University, Montreal, QC, CA, (2) McGill University Health Centre, Montreal, QC, CA,
SU-B-TRACK 2-3Validation of Handheld Mechanical 3D Ultrasound for Bedside Monitoring of Cartilage Degradation and Synovium Inflammation in Knee Osteoarthritis and Rheumatoid Arthritis
S Papernick*1,2, R Dima2, D Gillies1,2, T Appleton2,3, A Fenster1,2, (1) Robarts Research Institute, London, ON, CA, (2) Western University Canada, London, ON, CA, (3) Schulich School of Medicine and Dentistry, London, ON, CA
SU-CD-TRACK 2-4A Data Driven Fully Automated Contouring and Planning Solution for Cervical Cancer
D Rhee1*, A Jhingran1, B Rigaud1, K Huang1, K Kisling2, B Beadle3, C Cardenas1, S Kry1, S Vedam1, L Zhang1, K Brock1, W Shaw4, D O'Reilly4, H Simonds5, L Court1, (1) University of Texas MD Anderson Cancer Center, Houston, TX, (2) UC San Diego, La Jolla, CA, (3) Stanford University, Stanford, CA, (4) University of the Free State, Bloemfontein, ,ZA, (5) Stellenbosch University, Stellenbosch, ,ZA,
SU-CD-TRACK 2-6Deep-Learning Assisted Automatic Segmentation of Interstitial Needles in 3D Ultrasound Based High Dose-Rate Brachytherapy of Prostate Cancer
B Han1*, F Wang2, L Xing1, H Bagshaw1, M Buyyounouski1, (1) Stanford Univ School of Medicine, Stanford, CA, (2) Tsinghua University, Beijing,China
SU-CD-TRACK 2-11Detection and Segmentation of Brain Metastases On MR Images Using Machine Learning and a Novel Optimized Thresholding Technique
D Hsu*, A Ballangrud, L Cervino, J Deasy, A Li, H Veeraraghavan, M Hunt, A Shamseddine, M Aristophanous. Memorial Sloan-Kettering Cancer Center, New York, NY
SU-D-TRACK 1-1One to Many Modality Unsupervised Domain Adaptation for Multiple MRI Sequence Abdomen Organ Segmentation in MR-Guided Radiotherapy
J Jiang1*, H Veeraraghavan2, (1) MSKCC, New York, NY, (2) Memorial Sloan Kettering Cancer Center, New York, NY
TH-AB-TRACK 4-7Deep Learning-Based Auto-Segmentation of Swallowing and Chewing Structures
A Iyer*, M Thor, R Haq, J Deasy, A Apte, Memorial Sloan Kettering Cancer Center, New York, NY
TH-AB-TRACK 4-8Dosimetric Validation of An Artificial Intelligence Encapsulated Knowledge Transfer Across Medical Domains Applied to Heart Contouring for Treatment Planning
C V Guthier1*, R Zeleznik1, J Weiss1, J Taron2, C Hancox1, D Bitterman1, D Kim1, R Punglia1, B Foldyna2, M Lu2, B Kann1, J Bredfeldt1, U Hoffmann2, H Aerts1, R Mak1, (1) Dana-Farber/Brigham and Women's Cancer Center and Harvard Medical School, Boston, MA, (2) Massachusetts General Hospital and Harvard Medical School, Boston, MA,
TH-D-TRACK 5-4Head and Neck CTV Decision Making and Automatic Contouring Results in More Consistent Radiotherapy Plans
C Cardenas1*, B Beadle2, T Lim1, J Yang1, A Olanrewaju1, R Douglas1, T Netherton1, L Zhang1, L Court1, (1) The University of Texas MD Anderson Cancer Center, Houston, TX, (2) Stanford University, Stanford, CA
TH-D-TRACK 5-5A Generalizable Contour Validation Method Using Deep Learning-Based Image Classification
Y Zhang*, F Ceballos, Y Liang, L Buchanan, X Li, Medical College of Wisconsin, Milwaukee, WI
TH-D-TRACK 5-6Impact of CT Scanner Acquisition and Reconstruction Methods On Pediatric Organ Autosegmentation Model Generalizability
Philip M. Adamson1*, Petr Jordan1, Vrunda Bhattbhatt1, Taly Gilat Schmidt2, (1) Varian Medical Systems, 3120 Hansen Way, Palo Alto, CA, USA, (2) Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI
TU-EF-TRACK 3-5Automation of Palliative Radiotherapy Treatment Planning Using Independent Models to Prevent Errors From Propagating Through the Planning Process
T Netherton1*, D Rhee2, C Cardenas3, C Chung4, A Klopp5, L Colbert6, C Nguyen7, V Kolluru8, R Douglas9, C Peterson10, R Howell11, P Balter12, L Court13, (1) MD Anderson Cancer Center, Houston, TX, (2) MD Anderson Cancer Center, Houston, TX, (3) University of Texas MD Anderson Cancer Center, Houston, TX, (4) MD Anderson Cancer Center, Houston, TX, (5) MD Anderson Cancer Center, Houston, TX, AF, (6) Md Anderson Cancer Center, ,,(7) MD Anderson Cancer Center, Houston, TX, (8) Ut Md Anderson Cancer Center, ,,(9) The University Of Texas Md Anderson Cancer Center, ,,(10) The University of Texas MD Anderson Cancer Center, Houston, TX, (11) The University of Texas MD Anderson Cancer Center, Houston, TX, (12) UT MD Anderson Cancer Center, Houston, TX, (13) UT MD Anderson Cancer Center, Houston, TX
WE-F-TRACK 1-1JACK FOWLER JUNIOR INVESTIGATOR COMPETITION WINNER: Joint Adversarial Generator-Segmentor for Unsupervised CT to MRI Synthesis Based MRI Lung Tumor Segmentation
J Jiang*, Y Hu, N Tyagi, A Rimner, J Deasy, S Berry, H Veeraraghavan, Memorial Sloan Kettering Cancer Center, New York, NY