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Keywords: image analysis
| MO-A-BRC-5 | Quantifying the Impact of Class Imbalance Handling Techniques On Medical Image Deep Learning Performance B Reber*, K Brock, The University of Texas MD Anderson Cancer Center, Houston, TX |
| MO-C930-IePD-F5-1 | Patient-Specific Nuclei Size and Cell Spacing Distribution Extraction From Histopathology Whole Slide Images for Treatment Outcome Prediction Modelling Y Zou1*, M Lecavalier-barsoum2, M Pelmus3, S Abbasinejad Enger1,2,4, (1) Medical Physics Unit, Department of Oncology, Faculty of Medicine, McGill University, Montreal, QC, CA, (2) Department of Radiation Oncology, Jewish General Hospital, Montreal, QC, Canada, (3) Department of Pathology, Faculty of Medicine, McGill University, Montreal, QC, CA, (4) Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, CA |
| MO-C930-IePD-F9-4 | A Comparison of Methods for Computing T1 in MOLLI Images A Moody1*, H Huber2, L Cox3, P Nathanielsz4, G Clarke1, (1) University of Texas Health Science Center, San Antonio, TX, (2) Texas Biomedical Research Institute, San Antonio, TX, (3) Wake Forest School Of Medicine, Winston-Salem, NC, (4) University Of Wyoming, Laramie, WY |
| MO-C930-IePD-F9-5 | Reducing Bias and Noise in T2* Measurements for Low-SNR Data: A Phantom Study G Anthony1,2*, X Zhang1,3, X Guan1, R Dharmakumar1,2,3, (1) Krannert Cardiovascular Research Center, Indiana University School of Medicine/IU Health Cardiovascular Institute, Indianapolis, IN 46202, (2) Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, (3) Department of Bioengineering, University of California in Los Angeles, Los Angeles, CA 90095 |
| MO-E115-IePD-F6-4 | 4D-THRIVE MRI for Internal Target Volume Estimation in Abdominal and Lung Radiation Therapy J Deng, T Aguilera, R Hannan, A Godley*, University of Texas Southwestern Medical Ctr, Dallas, Texas |
| PO-GePV-I-15 | CT Simulation Realism: What Are the Impacts of Simulation Parameters? I Montero1,2,4*, S Sotoudeh-Paima1,2,3,5, E Abadi1,2,3,4,5, E Samei1,2,3,4,5, (1) Duke University Health System, Durham, NC, (2) Center for Virtual Imaging Trials, Durham, NC,(3) Carl E. Ravin Advanced Imaging Laboratories, Durham, NC, (4) Medical Physics Graduate Program, Duke University School of Medicine, Durham, NC, (5) Department of Electrical and Computer Engineering, Duke University, Durham, NC |
| PO-GePV-I-25 | Real-Time Quality Control of RF Coils Using Clinical MRI Data T McKeown1*, S Robertson2*, E Samei2, (1) Duke University, Durham, NC, (2) Duke University Health System, Durham, NC |
| PO-GePV-I-51 | Development and Verification of a Subregional Radiomic Framework for Image Segmentation J Gu1*, B Li1, J Zhu2, (1) Southeast University, Nanjing, JiangSu, CN, (2) Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, CN |
| PO-GePV-I-65 | Standardizing Radiotherapy Structure Names with Multimodal Data: Deep Learning Approach P Bose1*, S Srinivasan2, P Turner3, W Sleeman4, J Palta5, R Kapoor6, P Ghosh7, (1) Virginia Commonwealth University, Richmond, VA, (2) Virginia Commonwealth University, ,,(3) ,,,(4) Virginia Commonwealth University, Richmond, VA, (5) Virginia Commonwealth University, Richmond, VA, (6) VCU Health System, Richmond, VA, (7) Virginia Commonwealth University, Richmond, |
| PO-GePV-I-70 | Bridging the Gap Between Machine Learning and Clinicians Through Interpretable AI in Head and Neck Cancer Assessment Y Wang1, W Duggar2*, T Thomas2, P Roberts2, R Gatewood2, S Vijayakumar2, L Bian1, H Wang1, (1) Mississippi State University, ,,(2) University of Mississippi Med. Center, Jackson, MS, |
| PO-GePV-I-78 | Comparison of 18F-Choline and 18F-PSMA Performance On Detecting Malignant Lesions Using Fuzzy C-Means Segmentation On PET-CT Images of Patients with Recurrent Prostate Cancer M Katsikakis1, I Gatos1, N Papathanasiou2, S Tsantis1, D Apostolopoulos2, K Papadimitropoulos2, M Spiliotopoulou2, E Liatsikos3, D Mihailidis4, G C Kagadis1*, (1) Department of Medical Physics, University of Patras, Rion, GR 26504, GR, (2) Department of Nuclear Medicine, University of Patras, Rion, GR 26504, GR, (3) Department of Urology, University of Patras, Rion, GR 26504, GR, (4) University of Pennsylvania, Wynnewood, PA, USA |
| PO-GePV-I-82 | Predicting F-18 FMISO PET Hypoxia Measurements From F-18 FDG PET Scan Using a Generative Adversarial Network W Zhao*, M Grkovski, N Lee, H Schoder, J Humm, H Veeraraghavan, J Deasy, Memorial Sloan Kettering Cancer Center, New York, NY |
| PO-GePV-I-98 | Automatic Detector QC and Image Quality Assessment in Digital Radiography R DiTusa*, J So, Y Liu, B Peng, H Hsu, P Chaudhary, T Lin, S Jambawalikar, Columbia University Medical Center, New York, NY |
| PO-GePV-M-12 | Correlations of Evaluation Metrics in Deep Learning-Based Segmentation H Tan*, Y Xiao, R McBeth, S Lee, University of Pennsylvania, Philadelphia, PA |
| PO-GePV-M-48 | Automatic Quantification of Organ at Risk Contour Overlap and Separation in Radiotherapy J Brooks1*, A Anand2, R Foote1, D Hobbis2, A Jackson3, J Lucido1, J Ma1, D Pafundi3, S Patel2, Y Rong2, D Routman1, B Stish1, E Tryggestad1, S Yaddanapudi3, D Moseley1, (1) Mayo Clinic, Rochester, MN (2) Mayo Clinic Arizona, Phoenix, AZ (3) Mayo Clinic Jacksonville, Jacksonville Beach, FL |
| PO-GePV-M-50 | A Radiomics Application of the Two-Stage Clustering Algorithm for Finding Classes From a Large Sample with Many Variables Y Watanabe*, N Gopishankar, (1) University of Minnesota, Minneapolis, MN, (2) AIIMS, Delhi, India |
| PO-GePV-M-62 | Novel Quantitative Tool for Assessing Pulmonary Disease Burden in COVID-19 Using Ultrasound H Sagreiya1*, M Jacobs2, A Akhbardeh2, (1) University of Pennsylvania, Philadelphia, PA, (2) Johns Hopkins University, Baltimore, MD |
| PO-GePV-M-66 | Evaluation of Repeatability and Reproducibility of Radiomic Features Produced By The Fan-Beam KV-CT On A Novel Ring Gantry-Based PET/CT Linear Accelerator Using Two IBSI-Compliant Software Packages T Ketcherside1, A Sundquist2, C Han1, W Watkins1, L Court3, C Huntzinger2, Q Chen1, T Williams1, A Liu1*, (1) City of Hope Medical Center, Duarte, California, (2) Reflexion Medical Inc, Hayward, CA (3) UT MD Anderson Cancer Center, Houston, TX, |
| PO-GePV-M-152 | Evaluation of CT-On-Rails Image Quality Reproducibility for Intensity Modulated Proton Adaptive Therapy Y Hao*, W Smith, A Darafsheh, B Sun, T Zhang, T Zhao, Washington University School of Medicine in St. Louis |
| PO-GePV-M-291 | A Semi-Supervised Learning Method Using Soft-Label for Cell Nuclei Segmentation On Immunohistochemistry Images J Zhou1*, Z Yan2, J Polf3, H Zhang4, B Zhang5, M MacFarlane6, D Han7, M Zakhary8, A Gopal9, J Xu10, S Lee11, H Xu12, G Lasio13, S Chen14, (1) University of Maryland Shore Medical Center at Easton, Easton, MD, (2) Sense Brain, Princeton, NJ,(3) University of Maryland School of Medicine, Baltimore, MD, (4) University of Maryland School of Medicine, Baltimore, MD, (5) University of Maryland School of Medicine, Baltimore, MD, (6) University of Maryland School of Medicine, Baltimore, MD, (7) University of Maryland School of Medicine, Baltimore, MD, (8) University of Maryland School of Medicine, Baltimore, MD, (9) University of Maryland School of Medicine, Baltimore, MD, (10) University of Maryland School of Medicine, Baltimore, MD, (11) University of Maryland School of Medicine, Baltimore, MD, (12) University of Maryland School of Medicine, Baltimore, MD, (13) University of Maryland School of Medicine, Baltimore, MD, (14) University of Maryland School of Medicine, Baltimore, MD |
| PO-GePV-M-293 | Evaluating the Relationship Between MR Image Quality Measures and Deep Learning-Based Brain Tumor Segmentation Accuracy R Muthusivarajan1*, A Celaya2, J Yung3, S Viswanath4, D Marcus5, C Chung6, D Fuentes7, (1) UT MD Anderson Cancer Center, ,,(2) MD Anderson Cancer Center, Houston, TX, (3) UT MD Anderson Cancer Center, Houston, TX, (4) Case Western Reserve University, ,,(5) ,,,(6) The University of Texas MD Anderson Cancer Center, Houston, TX, (7) UT MD Anderson Cancer Center, Houston, TX |
| PO-GePV-M-337 | A 2D/3D Integrated Network for Breast Tumor Classification X Chen1, X Wang1*, J Lv1, Z Zhou2, (1) Xi'an Jiaotong University, Xi'an, 61, CN, (2)University Of Kansas Medical Center,Kansas City, KS. |
| PO-GePV-M-338 | Establishing Reliable Medical Prediction Model Based On Uncertainty Estimation D Feng1, X Chen1, X Wang1*, Z Zhou2, (1) Xi'an Jiaotong University, Xi'an, 61, CN, (2) University Of Kansas Medical Center,Kansas City, KS. |
| PO-GePV-M-349 | Quantitative Evaluation of Radiodermatitis Following Whole-Breast Radiotherapy with Various Color Space Models: A Feasibility Study S Park1*, J Kim2, C Choi2, J Park2, J Kim2, (1) Veterans Health Service Medical Center, Seoul, KR, (2) Seoul National University Hospital, Seoul, KR |
| PO-GePV-T-91 | Study On Correction Accuracy of Couch Sag in CT-Linac L Yu, B Yang, X Liu, T Pang, J Qiu*, Peking Union Medical College Hospital, Beijing, 11CN, |
| PO-GePV-T-114 | Dosiomics in the Prediction of Local Recurrences of Non-Small Cell Lung Cancers Treated with Stereotactic Body Radiation Therapy M diMayorca1*, T Wilhite1, M Tavakoli1, K Nie2. (1) University of Pittsburgh School of Medicine and UPMC Hillman Cancer Center, Pittsburgh, PA, (2) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ. |
| SU-E-BRB-5 | Comparing Transfer Learning, Data Augmentation, and Data Expansion in the Improvement of Medical Image Generation M Woodland1,2*, J Wood1, B Anderson4, S Kundu1, E Lin1, E Koay1, B Odisio1, C Chung1, H Kang1, A Venkatesan1, S Yedururi1, B De1, Y Lin1, A Patel2,3, K Brock1, (1) The University of Texas M.D. Anderson Cancer Center, Houston, TX, (2) Rice University, Houston, TX, (3) Baylor College of Medicine, Houston, TX, (4) University of California San Diego, San Diego, CA |
| SU-F-201-1 | A Weakly-Supervised Approach for Automatic Selection of Uniform Regions in Clinical CT Images W Cao*, P Korfiatis, T Kline, M Callstrom, A Missert, Mayo Clinic, Rochester, MN |
| SU-F-202-5 | Use of Automation in Image Quality Analysis in PET T Moretti*, S Leon, C Schaeffer, M Arreola, University of Florida, Gainesville, FL |
| SU-F-BRB-7 | Radiation Oncology Enterprise-Wide Performance Evaluation of Commercial AI-Based Automated Image Segmentation Software Solutions D Maes1*, E Gates1, P Forouzannezhad1, J Kang1, A Lim1, M Lavilla2, D Melancon1, B Nguyen2, Y Tseng1, E Weg1, J Meyer1, S Bowen1,3, (1) University of Washington, Department of Radiation Oncology, (2) Seattle Cancer Care Alliance, (3) University of Washington, Department of Radiology |
| SU-H300-IePD-F8-3 | Application of Lambda: Subvoxel Image Similarity in Complex Images P Boyle*, M Lauria, B Stiehl, L Naumann, A Santhanam, D Low, UCLA, Los Angeles, CA |
| SU-H300-IePD-F8-5 | Influence of Threshold Calibration On CT Image Quality of a Photon-Counting Detector P Rodesch*, D Richtsmeier, L Zalavari, M Bazalova-Carter, University of Victoria, Victoria, BCCA, |
| SU-H330-IePD-F9-4 | A Combined Radiomic and Clinical Model for the Differential Diagnosis of Pneumonitis in Patients with NSCLC (Non-Small Cell Lung Cancer) Patients Receiving Immunotherapy (IO Therapy) A Traverso1*, F Tohidinezhad1, D Bontempi1, A Dekker1, L Hendriks2, D De Ruysscher1, (1) MAASTRO Clinic, Maastricht, NL (2) Maastricht University Medical Centre, Maastricht, NL |
| SU-H330-IePD-F9-6 | A Radiogenomics Study Using a Network-Based Unbalanced Optimal Mass Transport Method in Head and Neck Squamous Cell Carcinoma J Oh1, H Veeraraghavan1, E Katsoulakis2, A Apte1, J Zhu3*, Y Yu1, N Lee1, V Hatzoglou1, A Tannenbaum3, N Riaz1, J Deasy1, (1) Memorial Sloan Kettering Cancer Center, New York, NY, (2) Department of Veterans Affairs, Tampa, FL, (3) Stony Brook University, Stony Brook, NY |
| SU-H430-IePD-F6-2 | Need for PET/CT Harmonization in Multi-Center Immunotherapy Radiomics Studies K Strasek1*, R Jeraj1,2, M Namias3, D Valentinuzzi1, (1) University of Ljubljana, Faculty for Mathematics and Physics, Ljubljana,SI, (2) University of Wisconsin, Madison, WI, (3) Fundacion Centro Diagnostico Nuclear, Buenos Aires, B, AR |
| SU-H430-IePD-F6-3 | Curation of a Large Public Head and Neck Dataset for Machine Learning (RADCURE) Using An Automated Data Mining Platform M Welch1, T Patel4, M Kazmierski3, J Marsilla3, S Huang1,2, S Kim3, K Rey-mcintyre1, B O'Sullivan1,2, J Waldron1,2, N Becker5, S Bratman1,2, A Hope1,2, B Haibe-kains3, T Tadic1,2*, (1) Princess Margaret Cancer Centre, University Health Network, Toronto, ON, CA,(2) Department of Radiation Oncology, University Of Toronto, Toronto, ON, CA, (3) Department of Medical Biophysics, University of Toronto, ON, CA, (4) Techna Institute, University Health Network, Toronto, ON, CA, (5) BCCA - Kelowna, Kelowna, BC, CA |
| SU-H430-IePD-F6-4 | The Medical Imaging and Data Resource Center (MIDRC) Technology Development Project (TDP) 3c: Developing Tools to Assist in Task-Specific Performance Evaluation for Machine Learning Algorithms Employing MIDRC Data K Drukker1, B Sahiner2, T Hu2, G Kim3, H Whitney1,4, N Baughan1, K Myers5, M Giger1, M McNitt-Gray3*, (1) University of Chicago, Chicago, IL, (2) US Food and Drug Administration, Silver Spring, MD, (3) David Geffen School of Medicine at UCLA, Los Angeles, CA, (4) Wheaton College, Wheaton, IL, (5) US Food and Drug Administration (retired),Phoenix, AZ |
| SU-J-201-7 | Patient Vs. Phantom: The Impact of Post-Processing On Image Quality of Chest Radiographs V Yadav*, E Macdonald, N Lafata, J Wilson, E Samei, Duke University Health System, Durham, NC |
| TH-D-201-1 | Comprehensive Size- and Kernel-Dependent Comparison of Image Quality Between Photon-Counting and Energy Integrating CT M Bhattarai1*, S Bache2, E Abadi1 E Samei1,2, (1) Duke University, Durham, NC, (2) Duke University Health System, Durham, NC |
| TH-E-BRC-1 | A Systematic Comparison of DNN-Based Lung CT Elastography Using Auto-Encoder, U-Net, Conditional and Cycle Generative Adversarial Network Techniques B Stiehl*, M Lauria, L Naumann, D O'Connell, P Boyle, I Barjaktarevic, D Low, A Santhanam, UCLA, Los Angeles, CA |
| TH-F-201-6 | Validation of Dual-Energy CT Based Composition Analysis Using Fresh Animal Tissues and Novel, Non-Epoxy Tissue Equivalent Samples K Niepel1*, S Tattenberg12, R Marants3, T Bortfeld2, J Verburg2, G Landry41, A Sudhyadhom3, K Parodi1, (1) Ludwig-Maximilians-Universitat Munchen, Garching, BV, DE, (2) Massachusetts General Hospital and Harvard Medical School, Boston, MA, (3) Brigham and Women's Hospital, Boston, MA, (4) University Hospital, LMU Munich, Munich, BV, DE |
| TU-D930-IePD-F2-5 | Deep Learning-Based Prognosis Prediction of Diffuse Large B-Cell Lymphoma in PET Images C Qian1;3*, C Jiang2, X Kai1, D Cao3, J Sun1, G Liugang1, N Xinye1, (1) The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, Jiangsu, CN, (2) Nanjing Drum Tower Hospital, Nanjing, Jiangsu, CN, (3) Changzhou Institute of Technology, Changzhou, Jiangsu, CN |
| TU-D930-IePD-F8-4 | Extraction of Osteosclerotic Regions Using Diffusion Equation K Doi1*, Y Anetai2, H Takegawa2, Y Koike2, S Nakamura2, T Nishio1, (1) Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Suita city, Osaka, JP, (2) Department of Radiation Oncology, Kansai Medical University Hospital, Hirakata city, Osaka, Japan |
| TU-GH-BRB-3 | Augmented Colorectal Cancer Detection Using Self-Attention-Incorporated Deep Learning X Jia1, S Sang1, Y Zhou2, H Ren1, M Laurie1, M Islam1, O Eminaga1, J Liao1, L Xing1*, (1) Stanford University School of Medicine, Stanford, CA, (2) University of California Santa Cruz, Santa Cruz, CA |
| TU-I345-IePD-F5-2 | A Radiomics-Integrated Deep-Learning Model for Identifying Radionecrosis Following Brain Metastasis Stereotactic Radiosurgery J Zhao1*, Z Yang1, Z Hu2, E Vaios1, D Carpenter1, S Floyd1, K Lafata1, F Yin1, C Wang1, (1) Duke University, Durham, NC, (2) Duke Kunshan University, Kunshan, Jiangsu, China |
| TU-J430-BReP-F1-5 | Virtual Non Contrast Tomography Synthesis for Hepatocellular Carcinoma Patients Using Multimodality-Guided Synergistic Neural Network J Chen1, W Li2, H Xiao3*, S Lam4, J Chen5, C Liu6, A Cheung7, J Cai8, (1)The Hong Kong Polytechnic University,Hung Hom, ,HK, (2) The Hong Kong Polytechnic University, ,,(3) The Hong Kong Polytechnic University, Hong Kong, 91, CN, (4) Duke Kunshan University, Kunshan, ,CN, (5) ,,,(6) The Hong Kong Polytechnic University, Hong Kong, Hong Kong, (7) ,,,(8) Hong Kong Polytechnic University, Hong Kong, ,CN |
| WE-B-201-2 | Development and Validation of a Radiomics Model to Predict Pathological Complete Response to Neoadjuvant Chemoradiation (nCRT) in Locally Advanced Rectal Cancer: A Perspective Observational Study Y Zhang1*, S Hu1, L Shi2, X Sun2, N Yue1, K Nie1, (1) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (2) Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang Univ., Hangzhou, ,CN |
| WE-B-201-6 | Radiomics of Tuberous Sclerosis Complex for Precision Diagnosis F Tixier1*, D Rodriguez1, J Jones1, M Islam2,3, M Hester4,5,6, M Ho1, (1) Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio, USA (2) Division of Neurology, Nationwide Children's Hospital, Columbus, OH, USA (3) Department of Clinical Pediatrics, Section of Neurology, The Ohio State University College of Medicine, Columbus, OH, USA (4) Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, USA (5) The Steve and Cindy Rasmussen Institute for Genomic Medicine, Columbus, Ohio, USA (6) Department of Neuroscience, College of Medicine, Ohio State University, Columbus, OH, USA |
| WE-C1000-IePD-F1-1 | Using CycleGAN Machine Learning to Increase Accuracy of Relative Electron Density and Proton Stopping Power Ratio J Scholey1*, L Vinas2, V Kearney3, S Yom4, P Larson5, M Descovich6, A Sudhyadhom7, (1) University of California San Francisco, San Francisco, CA, (2) University of California Berkeley, Berkeley, CA, (3) University of California San Francisco, San Francisco, CA, (4) University of California San Francisco, San Francisco, CA, (5)(6) University of California San Francisco, San Francisco, CA, (7) Brigham and Women's Hospital | Dana-Farber Cancer Institute | Harvard Medical School, Boston, MA |
| WE-C1000-IePD-F1-4 | The Potential Role of Radiomic-Driven Treatment Decision-Making for Locally Advanced NPC X Han1, X Teng1, J Zhang1, Z Ma1*, S Lam1, H Xiao1, C Liu1, W Li1, Y Huang1, F Lee2, J Cai1, (1) The Hong Kong Polytechnic University, Hong Kong, (2) Queen Elizabeth Hospital, Hong Kong |
| WE-C1000-IePD-F6-1 | Active Bone Marrow Delineation in a Swine Model Via Proton Density Fat Fraction MRI and Multi-Energy CT D Coleman1*, J Meudt1, D Shanmuganayagam1, J Shah2, A McMillan1, A Pirasteh1, J Miller1, B Bednarz1, M Lawless1, (1) University of Wisconsin, Madison, WI (2) Siemens Healthineers, Durham, NC |
| WE-C930-IePD-F5-4 | Deep Learning Improves Image Quality and Radiomics Reproducibility for High-Speed Four-Dimensional Computed Tomography Reconstruction B Yang*, X Chen, S Yuan, Y Liu, K Men, J Dai, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Beijing,China |