2024
DOI: 10.1002/acm2.14296
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Semi‐supervised auto‐segmentation method for pelvic organ‐at‐risk in magnetic resonance images based on deep‐learning

Xianan Li,
Lecheng Jia,
Fengyu Lin
et al.

Abstract: Background and purposeIn radiotherapy, magnetic resonance (MR) imaging has higher contrast for soft tissues compared to computed tomography (CT) scanning and does not emit radiation. However, manual annotation of the deep learning‐based automatic organ‐at‐risk (OAR) delineation algorithms is expensive, making the collection of large‐high‐quality annotated datasets a challenge. Therefore, we proposed the low‐cost semi‐supervised OAR segmentation method using small pelvic MR image annotations.MethodsWe trained a… Show more

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