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).
Original languageEnglish
Article numberPO-1691
Pages (from-to)S1417-S1418
JournalRadiotherapy and Oncology
Issue numberSuppl 1
Publication statusPublished - 2021
EventEuropean Society Radiation Oncology 2021 - Madrid, Madrid, Spain
Duration: 2021 Aug 262021 Aug 31

Subject classification (UKÄ)

  • Medical Image Processing


Dive into the research topics of 'A deeply supervised convolutional neural network ensemble for multilabel segmentation of pelvic OARs'. Together they form a unique fingerprint.

Cite this