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Technical Note: Dose prediction for radiation therapy using feature‐based losses and One Cycle Learning
Author(s) -
Zimmermann Lukas,
Faustmann Erik,
Ramsl Christian,
Georg Dietmar,
Heilemann Gerd
Publication year - 2021
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14774
Subject(s) - feature (linguistics) , histogram , computer science , artificial intelligence , mathematics , pattern recognition (psychology) , philosophy , linguistics , image (mathematics)
Purpose To present the technical details of the runner‐up model in the open knowledge‐based planning (OpenKBP) challenge for the dose–volume histogram (DVH) stream. The model was designed to ensure simple and reproducible training, without the necessity of costly advanced generative adversarial network (GAN) techniques. Methods The model was developed based on the OpenKBP challenge dataset, consisting of 200 and 40 head‐and‐neck patients for training and validation, respectively. The final model is a U‐Net with additional ResNet blocks between up‐ and down convolutions. The results were obtained by training the model with AdamW with the One Cycle scheduler. The loss function is a combination of the L1 loss with a feature loss, which uses a pretrained video classifier as a feature extractor. The performance was evaluated on another 100 patients in the OpenKBP test dataset. The DVH metrics of the test data were evaluated, where D 0.1 c c , and D mean were calculated for the organs at risk (OARs) and D 1 % , D 95 % , and D 99 %were computed for the target structures. DVH metric differences between predicted and true dose are reported in percentage. Results The model achieved 2nd and 4th place in the DVH and dose stream of the OpenKBP challenge, respectively. The dose and DVH score were 2.62 ± 1.10 and 1.52 ± 1.06, respectively. Mean dose differences for the different structures and DVH parameters were within ±1%. Conclusion This straightforward approach produced excellent results. It incorporated One Cycle Learning, ResNet, and feature‐based losses, which are common computer vision techniques.

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