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Taxonomy: IM- PET : Machine learning, computer vision
| BReP-SNAP-I-5 | An Empirical Comparison of Weka Classifiers for Outcome Prediction Using An Imaging Habitats Definition and Feature Extraction Method On MRI Q Han1*, R Palm2, K Latifi2, E Moros2, A Naghavi2, G Zhang2, (1) University of South Florida, Tampa, FL, (2) H. Lee Moffitt Cancer Center, Tampa, FL |
| BReP-SNAP-I-10 | Classification of Optical Coherence Tomography Images Using Deep Neural Networks J Kotoku1*, T Tsuji1, Y Hirose1, K Fujimori1, T Hirose1, A Oyama1, Y Saikawa1, T Mimura2, K Shiraishi2, T Kobayashi1, A Mizota2, (1) Graduate School of Medical Care and Technology, Teikyo University, Itabashi-ku,JP, (2) Teikyo University School of Medicine,Itabashi-ku,JP |
| BReP-SNAP-M-13 | A Novel Real-Time Markerless Target Tracking Pipeline Based On Faster R-CNN for Lung Cancer Radiotherapy L Deng1,4*, Z Dai2, X Liang3,4, H Zhao4, H Quan1, Y Xie4, (1) Wuhan University, Wuhan, Hubei, CN, (2) The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, CN, (3) Stanford Univ School of Medicine, Stanford, CA, (4) Shenzhen Institute Of Advanced Technology,Shenzhen, Guangdong, CN |
| BReP-SNAP-M-15 | An Adversarial Machine Learning Framework and Biomechanical Model Guided Approach for Generating 3D Lung Tissue Elasticity From Low Dose End-Exhalation CT A Santhanam1, B Stiehl1*, M Lauria1, I Barjaktarevic1, S Hsieh2, D Low1, (1) University of California, Los Angeles, Los Angeles, CA, (2) Mayo Clinic, Rochester, MN |
| BReP-SNAP-M-49 | Deep Learning Augmented Proton Portal Imaging: A Phantom Study S Charyyev1*, Y Lei2, J Harms3, B Eaton4, M McDonald5, W Curran6, T Liu7, J Zhou8, R Zhang9, X Yang10, (1) Emory University, Atlanta, GA, (2) Emory University, Atlanta, GA, (3) Emory University, Atlanta, GA, (4) Emory University, Atlanta, ,(5) Emory University, Atlanta, GA, (6) Emory University, Atlanta, GA, (7) Emory University, Atlanta, GA, (8) Emory University, Atlanta, GA, (9) Dartmouth College, Lebanon, NH, (10) Emory University, Atlanta, GA |
| BReP-SNAP-M-51 | Deep Learning-Based Deformable Image Registration Method and Multiple Reference Registration Strategy for Tumor Target Tracking in 2D Cine MRI y zhang*, T Mazur, X Wu, H Li, H Gach, D Yang, Washington University in St. Louis, St. Louis, MO |
| BReP-SNAP-M-111 | Patient-Specific Deep Learning Model for Deformable Image Registration s amini*, Z Jiang, Y Chang, Y Mowery, L Ren, Duke University Medical Center, Cary, NC |
| BReP-SNAP-M-146 | Uncertainty-Aware Reconstructed Image Correction for Proton Computed Tomography Using Bayesian Deep Learning Y Nomura1*, S Tanaka1, J Wang1, H Shirato1, S Shimizu1, L Xing1,2, (1) Hokkaido University, Sapporo, Hokkaido, Japan, (2) Stanford University, Palo Alto, CA |
| MO-F-TRACK 2-4 | OARnet: 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 |
| PO-GeP-I-5 | A Deep Learning Approach On Cirrhosis Diagnosis Utilizing Ultrasound B-Mode Images of Segmented Liver Left Lobes Using Liver Biopsy as the Gold Standard P Drazinos1, I Gatos2, S Tsantis2, 3, P Zoumpoulis1, I Theotokas1, D Mihailidis4, G Kagadis2*, (1) Diagnostic Echotomography S.A.,Athens, GR, (2) University of Patras, Rion, GR, (3) University of West Attica, Athens, GR, (4) University of Pennsylvania, Philadelphia, PA, USA |
| PO-GeP-I-6 | A Deep Learning-Based End-To-End CT Reconstruction Method K Lu*, L Ren, F Yin, Duke University, Durham, NC |
| PO-GeP-I-107 | Estimation of X-Ray Energy Spectrum for CT Scanner From Percentage Depth Dose Measurement Y Hasegawa1*, A Haga1, D Sakata2, Y Kanazawa1, M Tominaga1, M Sasaki1, T Imae3, K Nakagawa3, (1) University of Tokushima, Tokushima, JP, (2) National Institute of Radiological Sciences, Chiba, JP, (3) The University of Tokyo Hospital, Tokyo, JP |
| PO-GeP-I-119 | Evaluation of Synthetic CT Generation From CBCT Using a Deep Learning Model A Haidari1,2*, D Granville2, E Ali1,2, (1) Carleton University, Ottawa, ON, CA, (2) The Ottawa Hospital Cancer Centre, Ottawa, ON, CA |
| PO-GeP-I-127 | Gray Matter-Based Radiomics and Machine Learning for the Diagnosis of Attention-Ddeficit/Hyperactivity Disorder S Zhao1*, Z Mu2, H Zhao3, J Qiu3, W Lu3, W Lu3, L Shi3, (1) Beijing Anding Hospital, Capital Medical University, Beijing, CN, (2) The Second Affiliated Hospital Of Shandong First Medical University, Taian, CN, (3) Shandong First Medical University & Shandong Academy Of Medical Sciences, Taian, CN |
| PO-GeP-I-170 | Predicting the Severity of White Matter Hyperintensities Using Structural MRI and Machine Learning W Lu1*, H Li2, L Zheng1, L Shi1, W Lu1, J Qiu1, (1) Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, Shandong, CN, (2) Shandong University Of Science And Technilogy, Qingdao, Shandong, CN |
| PO-GeP-I-190 | Shape Analysis in PET Images Using Convolutional Neural Nets: Limitations of Standard Architectures I Klyuzhin1,2*, A Rahmim1,2, (1) BC Cancer Research Centre, Vancouver, BC, CA, (2) University of British Columbia, Vancouver, BC, CA |
| PO-GeP-M-17 | A 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-30 | A 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-79 | Automatic 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-99 | Clinical 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-169 | Dosimetric Analysis of OARnet Auto-Delineations for Head and Neck Organs-At-Risk M H Soomro*, H Nourzadeh, V Leandro Alves, W Choi, J Siebers, University of Virginia, Charlottesville, VA |
| PO-GeP-M-241 | Impact of Deep Learning Based Image Quality Augmentation On CBCT Based Radiomics Analysis M Huang*, Z Zhang, J Lee, Z Jiang, T Niu, F Yin, L Ren, Duke University Medical Center, Cary, NC |
| PO-GeP-M-320 | Organ Segmentation From CT Images Using Super Perception Convolutional Neural Networks for Cervical Cancer Brachytherapy Z Zhang1*, S Wang1, Y He2, R Zhou1, Z Jin1, P Xie2, J Wei2, (1) Xiangya Hospital Central South of University, Changsha, Hunan,CN, (2) Perception Vision Medical Technology, Guangzhou, Guangdong,CN, |
| PO-GeP-M-347 | Quantification of Intrafraction Prostate Motion Using Detected Features in Sagittal 2D Cine-MR B Strbac*, C Brouwer, S Both, J Langendijk, D Yakar, S Al-uwini, |
| PO-GeP-M-388 | Study On Intelligent Treatment Planning for Left Breast Cancer X He*1, B Su2, R Zhao2, (1)Ruijin Hospital and Shanghai Jiaotong University School of Medicine,Shanghai,CN (2) Shanghai Pulmonary Hospital and Tongji University School of Medicine,Shanghai,CN |
| PO-GeP-M-390 | Synthetic Contrast Enhancement of Cone Beam Computed Tomography (CBCT) for Adaptive Radiotherapy O Dona*, Y Wang, D Horowitz, A Xu, J Rickman, C Wuu, Columbia Univ, New York, NY |
| SU-CD-TRACK 2-10 | Deriving Ventilation Imaging From Free Breathing Proton MRI Via Deep Convolutional Neural Network D Capaldi1*, F Guo2, L Xing3, G Parraga4, (1) Stanford University, Stanford, CA, (2) Sunnybrook Research Institute, (3) Stanford Univ School of Medicine, Stanford, CA, (4) Western University, London, ON, CA |
| SU-D-TRACK 1-1 | One 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 |
| SU-E-TRACK 1-2 | Phantom-Based Training Framework for Deep Convolutional Neural Network CT Noise Reduction N Huber*, A Missert, H Gong, S Leng, L Yu, C McCollough, Mayo Clinic, Rochester, MN |
| TU-C-TRACK 1-7 | 4D Radiomics: Impact of 4D Image Quality On Radiomic Analysis Z Zhang*, M Huang, Z Jiang, Y Chang, J Torok, F Yin, L Ren, Duke University Medical Center, Cary, NC |
| WE-F-TRACK 1-1 | JACK 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 |
| WE-F-TRACK 1-3 | Accelerating MRI Acquisition Using Cascaded Attention UNet with Prior Information V Agarwal*, J Balter, Y Cao, Univ Michigan, Ann Arbor, MI |