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Taxonomy: IM- MRI : Segmentation
| BReP-SNAP-M-12 | A 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-21 | Automatic CT Segmentation for Radiotherapy Treatment Planning: How Good Is Good Enough? W S Ingram*, L Dong, University of Pennsylvania, Philadelphia, PA |
| BReP-SNAP-M-24 | Automatic Target Segmentation and Uncertainty Prediction for Post-Prostatectomy Radiotherapy Planning Using Bayesian U-Net X Xu*, C Lian, A Wang, T Royce, R Chen, J Lian, D Shen, University of North Carolina at Chapel Hill, Chapel Hill, NC |
| BReP-SNAP-M-25 | Automatic 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-26 | Auto-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-28 | Beams-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-29 | Branch-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-45 | CT-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-54 | Deep-Learning-Based Autosegmentation Outperforms Atlas-Based Autosegmentation in a Clinical Cohort of Breast Cancer Patients JJE Kleijnen1*, A Akhiat2, MS Hoogeman1, SF Petit1, (1) Department of radiotherapy, Erasmus MC, Rotterdam, the Netherlands, (2) Elekta AB, Stockholm, Sweden |
| BReP-SNAP-M-58 | Development 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-69 | Evaluation of Two Different Systems for the Deformable Segmentation of a Lung Tumor in the Treatment of NSCLC Using SBRT Y Cao1*, D Ghosh2, J Gomez3,A Singh4, H Malhotra5, (1) University at Buffalo, Buffalo, NY, (2) Williamsville North High School, Buffalo, NY, (3) Roswell Park Comprehensive Cancer Center, Buffalo, NY, (4)Roswell Park Comprehensive Cancer Center, Buffalo, NY, (5)Roswell Park Comprehensive Cancer Center, Buffalo, NY |
| BReP-SNAP-M-91 | Incorporating 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-97 | Lifelong 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-112 | Plan 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-134 | Segmentation 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 1-5 | RmU-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 2-1 | BEST IN PHYSICS (MULTI-DISCIPLINARY): A Deep Learning Cardiac Substructure Pipeline for MR-Guided Cardiac Applications E Morris1,2*, A Ghanem3, M Dong4, S Zhu1, M Pantelic5, C Glide-Hurst1,2, (1) Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, MI (2) Department of Radiation Oncology, Wayne State University School of Medicine, Detroit, MI (3) Department of Clinical Oncology, Alexandria University, Alexandria, Egypt (4) Department of Computer Science, Wayne State University, Detroit, MI (5) Department of Radiology, Henry Ford Cancer Institute, Detroit, MI |
| MO-F-TRACK 2-3 | Intensity-Based Thresholding of Probability Maps in Deep-Learning-Based Segmentation N Bice*, N Kirby, R Li, T Bahr, J Rembish, M Agarwal, S Stathakis, M Fakhreddine, UT Health San Antonio, San Antonio, TX |
| MO-F-TRACK 2-6 | Multi-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-9 | A 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-19 | A 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-66 | Cross-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-135 | Impact 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-219 | Training 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-21 | A 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-53 | An Automated Contouring Workflow for Increased Standardization and Efficiency D Hoffman1*, J Meyers2, R Manger1, D Hoopes1, I Dragojevic1, (1) UC San Diego, La Jolla, CA, (2) MIM Software Inc., Cleveland, OH |
| PO-GeP-M-58 | An 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-80 | Automatic Segmentation of Prostate Bed in Post-Prostatectomy CT Images X Xu*, J Lian, Univ North Carolina, Chapel Hill, NC |
| PO-GeP-M-81 | Automatic 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-82 | Automatic 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-83 | Automatic 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-84 | Auto-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-104 | CNN Based Organ Segmentation On Chest Radiograph Using Synthetic X-Ray Image Reconstructed From MDCT Sihwan Kim1*, Chulkyun Ahn1, Changyong Heo1,3, Jong-Hyo Kim1,2,3, (1) Seoul National University, Seoul, KR, (2) Seoul National Univ. Hospital, Seoul, KR, (3) Advanced Institutes of Convergence Technology, Suwon, KR |
| PO-GeP-M-135 | Deep 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-137 | Deep 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-193 | Evaluating 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-199 | Evaluation 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-252 | Improved 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-263 | Interactive 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-280 | Learned 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-304 | Multi-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-307 | Multiple 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-308 | Multiple 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-310 | Normal 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-370 | Robustness 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-373 | Scoring Contour Agreement Using a Beta Distribution Model T Lim*, X Wang, J Yang, MD Anderson Cancer Center, Houston, TX |
| PO-GeP-M-374 | Segmentation 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-386 | Study 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-400 | The Impact of CT Reconstruction Kernels On Atlas Based Automatic Segmentation M Reyhan*, B Swann, I Vergalasova, N Yue, K Nie, R Singh, M McKenna, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ |
| PO-GeP-M-417 | Using 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-419 | Validation 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-432 | Weakly-Supervised Deep Learning Based Automatic Image Segmentation Via Deformable Image Registration W Chi123*, W Lu1, L Ma1, J Wu1, H Chen4,, M Tan23, X Gu1, (1) UT Southwestern Medical Center, Dallas, TX, (2) South China University Of Technology, Guangzhou, China, (3) Guangzhou Laboratory, Guangzhou, China, (4) Sun Yat-sen University, Guangzhou, China |
| PO-GeP-M-439 | Comparison 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-682 | Quantitative 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-790 | The Use of Artificial Intelligence to Auto-Segment Organs-At-Risk in Total Marrow Irradiation Treatment A Liu*, R Li, C Han, J Liang, D Du, A Shinde, S Dandapani, A Amini, S Glaser, J Wong, City of Hope Medical Center, Duarte, CA |
| SU-CD-TRACK 2-11 | Detection 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 |
| TH-AB-TRACK 4-7 | Deep 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-D-TRACK 5-4 | Head 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 5-6 | Impact 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 |