Pelvic U-Net: multi-label semantic segmentation of pelvic organs at risk for radiation therapy anal cancer patients using a deeply supervised shuffle attention convolutional neural network

Pelvic U-Net: multi-label semantic segmentation of pelvic organs at risk for radiation therapy anal cancer patients using a deeply supervised shuffle attention convolutional neural network

Abstract Background Delineation of organs at risk (OAR) for anal cancer radiation therapy treatment planning is a manual and time-consuming process. Deep learning-based methods can accelerate and partially automate this task. The aim of this study was to develop and evaluate a deep learning model fo...

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Bibliographic Details
Main Authors: Michael Lempart, Martin P. Nilsson, Jonas Scherman, Christian Jamtheim Gustafsson, Mikael Nilsson, Sara Alkner, Jens Engleson, Gabriel Adrian, Per Munck af Rosenschöld, Lars E. Olsson
Format: Article
Language:English
Published: BMC 2022-06-01
Series:Radiation Oncology
Subjects:
Radiation therapy
Semantic segmentation
Deep learning
Anal cancer
Organs at risk
Online Access:https://doi.org/10.1186/s13014-022-02088-1

Internet

https://doi.org/10.1186/s13014-022-02088-1

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