Autosegmentation based on different-sized training datasets of consistently-curated volumes and impact on rectal contours in prostate cancer radiation therapy

Autosegmentation based on different-sized training datasets of consistently-curated volumes and impact on rectal contours in prostate cancer radiation therapy

Background and purpose: Autosegmentation techniques are emerging as time-saving means for radiation therapy (RT) contouring, but the understanding of their performance on different datasets is limited. The aim of this study was to determine agreement between rectal volumes by an existing autosegment...

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Bibliographic Details
Main Authors: Caroline Elisabeth Olsson, Rahul Suresh, Jarkko Niemelä, Saad Ullah Akram, Alexander Valdman
Format: Article
Language:English
Published: Elsevier 2022-04-01
Series:Physics and Imaging in Radiation Oncology
Subjects:
Radiation therapy
Deep-learning
Autosegmentation
Prostate cancer
Rectum
CT
Online Access:http://www.sciencedirect.com/science/article/pii/S2405631622000367

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