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Taxonomy: IM/TH- image Segmentation: General (Most aspects)
| PO-GePV-M-257 | A Deep Learning-Based Physician-Specific OAR Segmentation Framework for Radiotherapy Treatment Planning Y Chen*, L Xing, L Yu, N Panjwani, J Obeid, M Gensheimer, H Bagshaw, M Buyyounouski, N Kovalchuk, B Han, Stanford University School of Medicine, Stanford, CA |
| PO-GePV-T-439 | Evaluation of A New Method to Measure Colony Growth In-Vitro R Holden*, J Park, A Lynnette Price, D Dunn, S Floyd, M Oldham, Duke University Medical Center, Durham, NC |
| TH-E-TRACK 4-5 | Small Convolutional Neural Networks for Efficient 3D Medical Image Segmentation A Celaya1, J Actor1,2, R Muthusivarajan1, E Gates1*, C Chung1, D Schellingerhout1, B Riviere2, D Fuentes1, (1) University Of Texas Md Anderson Cancer Center, Houston, TX, (2) Rice University, Houston, TX |
| TU-IePD-TRACK 4-6 | Deploying Deep Learning-Based Image Segmentation Models Via CERR A Iyer*, E LoCastro, J Deasy, A Apte, Memorial Sloan-Kettering Cancer Center, New York, NY |
| WE-D-TRACK 1-1 | Towards Contour Quality Assurance of Cardiac Structures with Automatic Segmentation C Uche1, H Geng2, J Yu3, E Gore4, Y Xiao5, (1) University of Pennsylvania, Philadelphia, PA, (2) University Of Pennsylvania, Philadelphia, PA,(3) NRG Oncology Statistics and Data Management Center, Philadelphia, PA,(4) Medical College of Wisconsin, Milwaukee, Wisconsin,(5) University of Pennsylvania, Philadelphia, PA |