Convolutional neural network‐based dosimetry evaluation of esophageal radiation treatment planning

Purpose A dosimetry evaluation model for treatment planning of esophageal radiation therapy is developed using a deep learning model. The model predicts dose volume histogram (DVH) from distance to target histogram (DTH) based on stacked de‐noise auto‐encoder (SDAE) and one‐dimensional convolutional...

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Veröffentlicht in:	Medical physics (Lancaster) 2020-10, Vol.47 (10), p.4735-4742
Hauptverfasser:	Jiang, Dashan, Yan, Hui, Chang, Na, Li, Teng, Mao, Ronghu, Du, Chi, Guo, Bin, Liu, Jianfei
Format:	Artikel
Sprache:	eng
Schlagworte:	distance to target histogram dose volume histogram Neural Networks, Computer one‐dimensional convolutional network Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated stacked de‐noise auto‐encoder
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