Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth

Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth

Background: It is time-consuming for oncologists to delineate volumes for radiotherapy treatment in computer tomography (CT) images. Automatic delineation based on image processing exists, but with varied accuracy and moderate time savings. Using convolutional neural network (CNN), delineations of v...

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Bibliographic Details
Main Authors: Hanna Sartor, David Minarik, Olof Enqvist, Johannes Ulén, Anders Wittrup, Maria Bjurberg, Elin Trägårdh
Format: Article
Language:English
Published: Elsevier 2020-11-01
Series:Clinical and Translational Radiation Oncology
Subjects:
Cervical cancer radiotherapy
Organs-at-risk
Clinical Target Volume
Automatic segmentation
Convolutional neural network
Online Access:http://www.sciencedirect.com/science/article/pii/S2405630820300756

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http://www.sciencedirect.com/science/article/pii/S2405630820300756

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