A deep learning based automatic segmentation approach for anatomical structures in intensity modulation radiotherapy.

Journal: Mathematical biosciences and engineering : MBE

Published Date: Sep 1, 2021

Abstract

OBJECTIVE: To evaluate the automatic segmentation approach for organ at risk (OARs) and compare the parameters of dose volume histogram (DVH) in radiotherapy.

Authors

Han Zhou

Jiangsu Provincial Key Laboratory of Special Robot Technology, Hohai University, Changzhou, China.
Yikun Li

Department of Radiation Oncology, Jinling Hospital, Nanjing, Jiangsu, 210002, China.
Ying Gu

Department of Radiation Oncology, Jinling Hospital, Nanjing, Jiangsu, 210002, China.
Zetian Shen

Department of Radiation Oncology The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210002, China.
Xixu Zhu

Department of Radiation Oncology, Jinling Hospital, Nanjing, Jiangsu, 210002, China.
Yun Ge

Keywords

Deep Learning Humans Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (34814260)

A deep learning based automatic segmentation approach for anatomical structures in intensity modulation radiotherapy.

Abstract

Authors

Keywords

External Resources

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