A deeply supervised convolutional neural network ensemble for multilabel segmentation of pelvic OARs

Forskningsoutput: TidskriftsbidragPublicerat konferensabstract

Sammanfattning

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).
Originalspråkengelska
ArtikelnummerPO-1691
Sidor (från-till)S1417-S1418
TidskriftRadiotherapy and Oncology
Volym161
UtgåvaSuppl 1
DOI
StatusPublished - 2021
EvenemangEuropean Society Radiation Oncology 2021 - Madrid, Madrid, Spanien
Varaktighet: 2021 aug 262021 aug 31
https://www.estro.org/

Ämnesklassifikation (UKÄ)

  • Medicinsk bildbehandling

Nyckelord

  • Deep Learning
  • Radiation therapy
  • Semantic segmentation
  • Anal cancer

Citera det här