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Taxonomy: IM- MRI : Segmentation
| MO-C930-GePD-F5-3 | Improving Differentiation of Tumor and Surrounding Tissues for Tumor Delineation in Pancreas Using Image Textures From Dual-Energy CT D Schott1*, G Noid1 , P Knechtges1 , W Hall1 , B Erickson1 , T Schmidt2 , X Li1 , (1) Medical College of Wisconsin, Milwaukee, WI, (2) Marquette University, Whitefish Bay, WI |
| MO-GH-SAN2-5 | Cardiac Substructure Segmentation with Deep Learning for Improved Cardiac Sparing E Morris1,2*, A Ghanem1,3, M Dong4, H Emami4 , M Pantelic5, E Walker1, C Glide-Hurst1,2, (1) Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan, (2) Department of Radiation Oncology, Wayne State University School of Medicine, Detroit, Michigan, (3) Department of Clinical Oncology, Alexandria University, Alexandria, Egypt, (4) Department of Computer Science, Wayne State University School of Medicine, Detroit, Michigan, (5) Departments of Radiology, Henry Ford Cancer Institute, Detroit, Michigan |
| MO-I345-GePD-F2-4 | TG263-Net: A Deep Learning Model for Organs-At-Risk Nomenclature Standardization D Rhee1*, C Nguyen2 , T Netherton3 , C Owens4 , L Court5 , C Cardenas6 , (1) MD Anderson Cancer Center, Houston, TX, (2) MD Anderson Cancer Center, Houston, TX, (3) MD Anderson Cancer Center, Houston, TX, (4) University of Texas MD Anderson Cancer Center, Houston, TX, (5) UT MD Anderson Cancer Center, Houston, TX, (6) University of Texas MD Anderson Cancer Center, Houston, TX |
| MO-K-SAN2-1 | Automatic Detection of Contouring Errors Using Convolutional Neural Networks D Rhee1*, C Cardenas2 , H Elhalawani3 , R McCarroll4 , L Zhang5 , J Yang6 , B Beadle7 , L Court8 , (1) MD Anderson Cancer Center, Houston, TX, (2) University of Texas MD Anderson Cancer Center, Houston, TX, (3) UT MD Anderson Cancer Center, Houston, TX, (4) University of Maryland Medical Center, Baltimore, MD, (5) MD Anderson Cancer Center, Houston, TX, (6) MD Anderson Cancer Center, Houston, TX, (7) Stanford University, Stanford, CA, (8) UT MD Anderson Cancer Center, Houston, TX |
| PO-GePV-I-7 | Applying Generative Adversarial Networks in the Target Segmentation Among Multiple MRI Imaging Modalities L Wu1*, L Xu2 , H Lin2 , (1) School of Information Science Technology, East China Normal University, Shanghai, China.(2) School of Computer and Information, Hefei University of Technology, Hefei, China. |
| PO-GePV-I-8 | Feasibility Study of Various Tumor Applicability in Deep Learning Based Automatic Tumor Delineation System Y Park*, K Kim , S Kang , T Suh , The Catholic University of Korea, College of Medicine Seoul |
| PO-GePV-I-21 | Comparison of Image Segmentation Algorithms Based On Threshold Technique and Clustering Technique for CT Scan Images M Mahdian Manesh , R Faghihi*, Shiraz universityShiraz |
| PO-GePV-M-13 | Capsule Architecture Based Automatic Lung Segmentation Strategy Y Liu1*, E Zhang2 ,X Gu3 , (1) Sichuan University, Chengdu, ,(2) The University of Texas Southwestern Medical Ctr, Dallas, TX, ,(3) UT Southwestern Medical Center, Dallas, TX |
| PO-GePV-M-22 | Brain Tumor Segmentation Basedon Features Extracted From MRI Multimodal Images Using Deep Convolution NeuralNetworks B Zhang*, H Lin , Z Xue , j xu , B Liu , Z Wei , School of Electronic Science Application Physics,Hefei University of Technology, Hefei, China |
| PO-GePV-P-78 | Using Stacked FCN Networks to Improve the Accuracy of Automatic Delineation of Target Volumes and Organs at Risk Y Fu*, H Yu , West China HospitalChengdu |
| SU-E-303-2 | Cross-Modality (MR-CT) Educed Deep Learning (CMEDL) for Segmentation of Lung Tumors On CT J Jiang1*, N Tyagi2 , Y Hu3 , A Rimner4 , S Berry5 , J Deasy6 , H Veeraraghavan7 , (1) MSKCC, New York, NY, (2) Memorial Sloan-Kettering Cancer Center, New York, NY, (3) Memorial Sloan Kettering Cancer Center, New York, NY, (4) Memorial Sloan-Kettering Cancer Center, New York, NY, (5) Memorial Sloan Kettering Cancer Center, New York, NY, (6) Memorial Sloan Kettering Cancer Center, New York, NY, (7) Memorial Sloan Kettering Cancer Center, New York, NY |
| SU-F-SAN2-2 | AI-Seg: An Artificial Intelligence (AI)-Based Automatic Organs at Risk(OAR) Contouring Platform for Head and Neck Cancer (H&N) Radiotherapy J Wu*, P Lynch , J Shah , W Lu , X Gu , UT Southwestern Medical Center, Dallas, TX |
| SU-F-SAN2-5 | Improving the Robustness of a Deep Learning Based Thoracic CT Segmentation Algorithm (DLSeg) Q Chen1*, X Feng2 , M Bernard1 , (1) University of Kentucky, Lexington, KY, (2) University of Virginia, Charlottesville, VA |
| SU-I300-GePD-F6-1 | Auto Segmentation of Male Pelvis On CBCT Using 3D U-Net R L.J. Qiu1*, T Ma1 , K Stephans1 , C Shah1 , A Godley2 , P Xia1 , (1) Cleveland Clinic, Cleveland, OH, (2) Miami Cancer Institute, Miami, FL |
| SU-I300-GePD-F6-3 | Effects of CT Image Acquisition and Reconstruction Parameters On Automatic Contouring Algorithms K Huang*, D Rhee , R Ger , R Layman , J Yang , C Cardenas , L Court , MD Anderson Cancer Center, Houston, TX |
| SU-I300-GePD-F6-4 | Evaluation of Abdominal Autosegmentation Versus Inter-Observer Variability On a High-Speed Ring Gantry CBCT System Philip M. Adamson*, Petr Jordan* , Varian Medical Systems, Palo Alto, CA |
| SU-I300-GePD-F6-6 | Using a Bayesian Neural Network Approximation to Quantify the Uncertainty in Segmentation Prediction On Prostate Cancer D Nguyen*, A Balagopal , C Shen , M Lin , R Hannan , S Jiang , Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA |
| SU-I430-GePD-F9-2 | Development of Multi-Atlas-Based Prostatic Urethra Identification Method Using Machine Learning H Takagi1*, N Kadoya2 , T Kajikawa2 , S Tanaka2 , Y Takayama2 , T Chiba2 , K Ito2 , S Dobashi1 , K Takeda1 , K Jingu2 , (1) Course of Radiological Technology, Health Sciences, Tohoku University Graduate School of Medicine, Sendai, 04, (2) Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, |
| SU-K-303-1 | Auto Segmentation of Organs at Risk in Thorax Computed Tomography Using Deep Convolutional Neural Networks M Haytmyradov1*, M Surucu1 , F Cassetta1 , J Roeske1 , (1) Loyola University Medical Center, Maywood, IL |
| SU-L-221CD-6 | Multiple Resolution Residual Network for Automatic Lung Tumor and Lymph Node Segmentation Using CT Images H Um*, J Jiang , A Rimner , L Luo , J Deasy , M Thor , H Veeraraghavan , Memorial Sloan Kettering Cancer Center, New York, NY |
| SU-L-225BCD-3 | An Independent Evaluation of a Deep Learning Research Tool for Autocontouring CT Images of Prostate Radiotherapy Patients D Granville1*, B Wilson1 , J Sutherland1,2 , D La Russa1,2 , M MacPherson1,2,3 , (1) The Ottawa Hospital, Ottawa, ON, (2) University of Ottawa, Ottawa, ON, (3) Carleton University, Ottawa, ON |
| SU-L-225BCD-5 | Automatic Quality-Assurance Method for Deep Learning-Based Segmentation in Radiotherapy with Convolutional Neural Networks K Men, J Dai*, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 10021, China |
| SU-L-225BCD-7 | Learning-Based Automatic Segmentation of Arteriovenous Malformations On Contrast CT Images in Brain Stereotactic Radiosurgery T Wang*, Y Lei , S Tian , X Dong , X Jiang , J Zhou , T Liu , S Dresser , W Curran , H Shu , X Yang , Emory Univ, Atlanta, GA |
| TH-A-225BCD-2 | Improving Deformable Image Registration of 4DCTs Using a Generative Adversarial Deep Neural Network for Automated Lung Lobe Segmentation B Stiehl1*, M Lauria1 , I Barjaktarevic1 , P Lee1 , D Low1 , A Santhanam1 , (1) University of California, Los Angeles, Los Angeles, CA |
| TH-BC-225BCD-3 | A Framework for Auto-Segmentation of Gross Tumor Volume Based On Multi-Parametric MRI Using Deep Learning Algorithms Y Liang, D Schott, Y Zhang, H Nasief, E Paulson, W Hall, P Knechtges, B Erickson, X Li, Medical College of Wisconsin, Milwaukee, WI |
| TH-BC-225BCD-11 | Fully Automated Multi-Organ Segmentation in Abdominal MRI with DenseUnet Y Chen1, 2*, J Xiao1 , L Wang1 , B Sun3 , Z Deng1 , Y Lao1, 4 , N Wang1, 2 , R Saouaf1 , R Tuli1 , D Li1, 2 , W Yang1, 4 , Z Fan1, 2 , (1) Cedars-Sinai Medical Center, Los Angeles, CA, USA (2) University of California Los Angeles, Los Angeles, CA, USA(3) Fujian Medical University Union Hospital, Fuzhou, Fujian, China (4)University of Southern California, Los Angeles, CA, USA |
| TU-AB-SAN2-7 | Development and Validation of Deep Learning Segmentation Network for Cardio-Pulmonary Substructure Segmentation R Haq*, A Hotca-Cho , A Apte , A Rimner , J Deasy , M Thor , Memorial Sloan Kettering Cancer Center, New York, NY |
| TU-AB-SAN2-12 | Identifying Oropharyngeal Clinical Target Volumes Delineation Patterns From Peer-Reviewed Clinical Delineations Via Cascade 3D Fully-Convolutional Networks C Cardenas1*, J Yang1 , A Mohamed1 , C Fuller1 , B Beadle2 , A Garden1 , L Court1 , (1) University of Texas MD Anderson Cancer Center, Houston, TX, (2) Stanford University, Stanford, CA |
| TU-C930-GePD-F9-1 | Automatic Segmentation On CBCT Images Using a Combination of CBCT Enhancement and Deep Learning CT Segmentation S Andersson*, R Nilsson, RaySearch Laboratories AB, Stockholm |
| TU-E-SAN2-1 | A Novel Training Strategy for Data with Incomplete Labeling in CNN-Based Head-And-Neck OAR Segmentation X Feng1*, K Qing2 , Q Chen3 , (1) University of Virginia, Charlottesville, VA, (2) Rutgers Cancer Institute of New Jersey, Bridgewater, NJ, (3) University of Kentucky, Lexington, KY |
| TU-E-SAN2-3 | Development of a Fast, Multi-Stage U-Net for Automatic Segmentation of Cardiac Substructures in Non-Contrast CT Images H Lin1*, J Zou1 , T Li1 , B Ky1 , J Bekelman1 , W Bosch2 , H Lu1 , W Kenworthy1 , B Teo1 , L Dong1 , (1) University of Pennsylvania, Philadelphia, PA, (2) Washington Univ, Saint Louis, MO |
| TU-E-SAN2-4 | Development of A Novel Mixing 2D-3D Fully Convolutional Neural Network for Pancreas Segmentation B Ye1 , X Qi2 , S Tan1* (1) Huazhong University of Science & Technology, Wuhan, China (2) UCLA School of Medicine, Los Angeles, CA |
| TU-E-SAN2-5 | Fully Automated Segmentation of 33 Abdominal Structures Using Deep Learning - Implications for Radiotherapy Dose Estimation A Weston*, P Korfiatis , K Philbrick , P Kostandy , A Zeinoddini , A Boonrod , N Takahashi, M Moynagh , B Erickson , Mayo Clinic, Rochester, MN |
| TU-E-SAN2-7 | Modified U-Net for High-Resolution High-Level Feature Extraction and Its Application to Liver-Tumor Segmentation H Seo1*, C Huang2 , L Xing1 , (1) Stanford Univ School of Medicine, Stanford, CA, (2) Stanford Univ School of Engineering and Medicine, Stanford, CA |
| TU-HI-SAN2-1 | A Novel Semantic CT Segmentation Algorithm Using Boosted Attention-Aware Convolutional Neural Networks V Kearney*, J Chan , T Wang , A Perry , S Yom , T Solberg , UCSF Comprehensive Cancer Center, San Francisco, CA |
| TU-J345-GePD-F5-4 | Multi-Organ Segmentation Through Surrogate Labels and Classification of Intermediate Network Representations D Huff1*, A Weisman1 , T Bradshaw1 , R Jeraj1,2 , (1) University of Wisconsin-Madison, Madison, Wisconsin, (2) University of Ljubljana, Ljubljana, Slovenia |
| TU-J345-GePD-F5-5 | Self-Attention Based Deep Learning Probabilistic Parotid Gland Segmentation Quality Evaluation Using Dose Volume Histogram Analysis S Berry*, J Jiang , S Elguindi , M Hunt , J Deasy , H Veeraraghavan , Memorial Sloan Kettering Cancer Center, New York, NY |
| WE-C1000-GePD-F2-3 | Development A Novel Convolutional Neural Network with Paced Transfer Learning for CT Based Liver Segmentation Z Zhang1*, X Pan2 , X Qi3 , (1) Xi'an University of Posts and Telecommunications, Xi'an,shaanxi, ,(2) Xi'an University of Posts and Telecommunications, Xi'an,shaanxi,(3) UCLA School of Medicine, Los Angeles, CA |
| WE-C1000-GePD-F2-4 | Hippocampal Segmentation From CT Scans with a Convolutional Nerual Network E Porter1*, P Fuentes2 , Z Siddiqui3 , A Thompson3 , T Guerrero3 , (1) Wayne State University, Detroit, MI, (2) Oakland University William Beaumont School of Medicine, Rochester, MI, (3) Beaumont Health, Royal Oak, MI |
| WE-C1000-GePD-F2-5 | Segmentation of Organs at Risk in Nasopharyngeal Cancer for Radiotherapy Using A Nested U-Net Architecture Fan SONG1,2*, Sihua WU1,3, Sijuan Huang1, Yunfei XIA1, Xin Yang1. (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) Guangdong University of Technology, Guangzhou, Guangdong, 511400, China. (3) Xinhua College of Sun Yat-sen University, Guangzhou, Guangdong, 510520,China. |
| WE-C1030-GePD-F5-2 | Automated Detection and Segmentation of Lung Tumors Using Deep Learning C Owens1,2*, D Rhee1,2 , D Fuentes3 , C Peterson2,4 , J Li5 , M Salehpour1 , L Court1,2,3 , J Yang1,2 , (1) Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, (2) The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, (3) Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, (4) Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, (5) Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX |
| WE-C1030-GePD-F5-3 | Clinical Assessment of Deep Learning-Based Auto Segmentation On Nasopharyngeal Cancer J Wang*, S Sun , W Hu , Z Zhang Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China |
| WE-C1030-GePD-F5-5 | Utilizing the Clique Atrous Spatial Pyramid Pooling for Pancreas Segmentation M Yang1 , X Qi2 , S Tan1 (1) Huazhong University of Science and Technology, Wuhan, China,(2) UCLA School of Medicine, Los Angeles, CA |
| WE-C1030-GePD-F5-6 | How Many Sample Sizes Are Appropriate for Deep Learning Based Auto Segmentation for Head and Neck Cancer? F Yingtao , W Hu*, J Wang , S Chen , S Sun , Z Zhang , Fudan University Shanghai Cancer Center, Shanghai |
| WE-C930-GePD-F8-3 | Interactive Deep Learning-Based Delineation of Gross Tumor Volume for Post-Operative Glioma Patients M Nordstrom1,2*, J Soderberg2 , N Shusharina3 , D Edmunds3 , F Lofman2 , H Hult1 , A Maki1 , T Bortfeld3 , (1) Royal Institute of Technology, Stockholm, (2) RaySearch Laboratories, Stockholm, (3) Massachusetts General Hospital, Boston |
