@inproceedings{96c28317213e4d55b2e66c9a5f50423d,
title = "Automatic learning-based selection of beam angles in radiation therapy of lung cancer",
abstract = "The treatment of lung cancer using external beam radiation requires an optimal selection of the radiation beam directions to avoid unnecessarily treatment of normal healthy tissues. We introduce an automated beam selection method, based on learning the relations between beam angles and anatomical features. Using a large dataset of clinical plans, we train a random forest regressor to predict beam angle likelihood. We then use an optimization procedure that incorporates inter-beam dependencies and selects the treatment beams. We present validation results, demonstrating the equivalence of automatically-selected beams and the derived radiation therapy plans to the clinical, manually-planned, ground-truth. The proposed method may be a useful clinical tool for reducing the manual planning workload, while sustaining plan quality.",
keywords = "Machine learning, Optimization, Radiation therapy planning",
author = "Guy Amit and Purdie, {Thomas G.} and Alex Levinshtein and Hope, {Andrew J.} and Patricia Lindsay and Jaffray, {David A.} and Vladimir Pekar",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 ; Conference date: 29-04-2014 Through 02-05-2014",
year = "2014",
month = jul,
day = "29",
doi = "10.1109/isbi.2014.6867851",
language = "English",
series = "2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "230--233",
booktitle = "2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014",
address = "United States",
}
