Application of deep neural networks for automatic planning in radiation oncology treatments

A. M. Barragán-Montero, D. Nguyen, W. Lu, M. Lin, X. Geets, E. Sterpin, S. Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Treatment planning for radiotherapy patients is a time-consuming and manual process. In this work, we investigate the use of deep neural networks to learn from previous clinical cases and directly predict the optimal dose distribution for a new patient. The proposed model combines two architectures, UNet and DenseNet, and used mean squared error as loss function. Ten input channels were used to include dosimetric and anatomical information. A set of 100 patients was used for training/validation and 29 for testing. Dice similarity coefficients ≥ 0.9 for the isodose-lines in the predicted versus the clinical dose showed the excellent accuracy of the model.

Original languageEnglish (US)
Title of host publicationESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
PublisherESANN (i6doc.com)
Pages161-166
Number of pages6
ISBN (Electronic)9782875870650
StatePublished - Jan 1 2019
Event27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019 - Bruges, Belgium
Duration: Apr 24 2019Apr 26 2019

Publication series

NameESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference

Conference27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019
CountryBelgium
CityBruges
Period4/24/194/26/19

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Barragán-Montero, A. M., Nguyen, D., Lu, W., Lin, M., Geets, X., Sterpin, E., & Jiang, S. (2019). Application of deep neural networks for automatic planning in radiation oncology treatments. In ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 161-166). (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). ESANN (i6doc.com).