Using supervised learning and guided monte carlo tree search for beam orientation optimization in radiation therapy

Azar Sadeghnejad Barkousaraie, Olalekan Ogunmolu, Steve Jiang, Dan Nguyen

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

Abstract

In clinical practice, the beam orientation selection process is either tediously done by the planner or based on specific protocols, typically yielding suboptimal and inefficient solutions. Column generation (CG) has been shown to produce superior plans compared to those of human selected beams, especially in highly non-coplanar plans such as 4π Radiotherapy. In this work, we applied AI to explore the decision space of beam orientation selection. At first, a supervised deep learning neural network (SL) is trained to mimic a CG generated policy. By iteratively using SL to predict the next beam, a set of beam orientations would be selected. However, iteratively using SL to select the next beam does not guarantee the plan’s quality. Although the teacher policy, CG, is an efficient method, it is a greedy algorithm and still finds suboptimal solutions that are subject to improvement. To address this, a reinforcement learning application of guided Monte Carlo tree search (GTS) was implemented, coupled with SL to guide the traversal through the tree, and update the fitness values of its nodes. To test the feasibility of GTS, 13 test prostate cancer patients were evaluated. Our results show that we maintained a similar planning target volume (PTV) coverage within 2% error margin, reduce the organ at risk (OAR) mean dose, and in general improve the objective function value, while decreasing the computation time.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Radiation Therapy - 1st International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsDan Nguyen, Steve Jiang, Lei Xing
PublisherSpringer
Pages1-9
Number of pages9
ISBN (Print)9783030324858
DOIs
StatePublished - Jan 1 2019
Event1st International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 17 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11850 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/17/1910/17/19

Keywords

  • Artificial intelligent
  • Beam orientation
  • Deep neural network
  • IMRT
  • Monte Carlo Tree Search
  • Prostate cancer
  • Radiation therapy

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Sadeghnejad Barkousaraie, A., Ogunmolu, O., Jiang, S., & Nguyen, D. (2019). Using supervised learning and guided monte carlo tree search for beam orientation optimization in radiation therapy. In D. Nguyen, S. Jiang, & L. Xing (Eds.), Artificial Intelligence in Radiation Therapy - 1st International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 1-9). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11850 LNCS). Springer. https://doi.org/10.1007/978-3-030-32486-5_1