• Corpus ID: 239024539

Predicting scenario doses for robust automated radiation therapy treatment planning

@inproceedings{Eriksson2021PredictingSD,
  title={Predicting scenario doses for robust automated radiation therapy treatment planning},
  author={Oskar Eriksson and Tianfang Zhang},
  year={2021}
}
Purpose: We present a framework for robust automated treatment planning using machine learning, comprising scenario-specific dose prediction and robust dose mimicking. Methods: The scenario dose prediction pipeline is divided into the prediction of nominal dose from input image and the prediction of scenario dose from nominal dose, each using a deep learning model with U-net architecture. By using a specially developed dose–volume histogram–based loss function, the predicted scenario doses are… 

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