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Taxonomy: IM/TH- Image Analysis (Single modality or Multi-modality): Image segmentation
| 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-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-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-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, |
| 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 2-3 | 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 |
| 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-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-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-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-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-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-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 |
| 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 |