PO-1691 A deeply supervised convolutional neural network ensemble for multilabel segmentation of pelvic OARs
| Autor: | M. Lempart, M.P. Nilsson, J. Scherman, M. Nilsson, C.J. Gustafsson, P. Munck af Rosenschöld, L.E. Olsson |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
business.industry Computer science medicine.medical_treatment Deep learning Hematology Image segmentation Convolutional neural network Radiation therapy medicine.anatomical_structure Oncology medicine Radiology Nuclear Medicine and imaging Segmentation Bone marrow Artificial intelligence Radiology business Radiation treatment planning Pelvis |
| Zdroj: | Radiotherapy and Oncology. 161:S1417-S1418 |
| ISSN: | 0167-8140 |
| DOI: | 10.1016/s0167-8140(21)08142-1 |
| Popis: | Accurate delineation of organs at risk (OAR) is a crucial step in radiation therapy (RT) treatment planning but is a manual and time-consuming process. Deep learning-based methods have shown promising results for medical image segmentation and can be used to accelerate this task. Nevertheless, it is rarely applied to complex structures found in the pelvis region, where manual segmentation can be difficult, costly and is not always feasible. The aim of this study was to train and validate a model, based on a modified U-Net architecture, for automated and improved multilabel segmentation of 10 pelvic OAR structures (total bone marrow, lower pelvis bone marrow, iliac bone marrow, lumosacral bone marrow, bowel cavity, bowel, small bowel, large bowel, rectum, and bladder). (Less) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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