Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models

Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models

Abstract Background Contour delineation, a crucial process in radiation oncology, is time-consuming and inaccurate due to inter-observer variation has been a critical issue in this process. An atlas-based automatic segmentation was developed to improve the delineation efficiency and reduce inter-obs...

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Bibliographic Details
Main Authors: Yuka Urago, Hiroyuki Okamoto, Tomoya Kaneda, Naoya Murakami, Tairo Kashihara, Mihiro Takemori, Hiroki Nakayama, Kotaro Iijima, Takahito Chiba, Junichi Kuwahara, Shouichi Katsuta, Satoshi Nakamura, Weishan Chang, Hidetoshi Saitoh, Hiroshi Igaki
Format: Article
Language:English
Published: BMC 2021-09-01
Series:Radiation Oncology
Subjects:
Artificial intelligence
Automatic segmentation
Deep learning neural network
Prostate cancer
Head and neck cancer
Online Access:https://doi.org/10.1186/s13014-021-01896-1

Internet

https://doi.org/10.1186/s13014-021-01896-1

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