Publication Details

Distributed Evolutionary Design of HIFU Treatment Plans

CHLEBÍK, J.; JAROŠ, J. Distributed Evolutionary Design of HIFU Treatment Plans. GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion. Lille: Association for Computing Machinery, 2021. p. 297-298. ISBN: 978-1-4503-8351-6.
Czech title
Distribuovaný Evoluční Návrh Ultrazvukových Operačních Plánů
Type
presentation, poster
Language
English
Authors
URL
Keywords

Evolutionary strategy, Island model, Farmer-Workers model, HIFU, Treatment planning, k-Wave

Abstract

High-Intensity Focused Ultrasound (HIFU) is a modern technique for non-invasive, non-ionising cancer treatment where malignant tissue is destroyed by thermal ablation. To perform such treatment, a plan consisting of a series of short sonications must be created. In recent years, a realistic thermal model using patient-specific materials was introduced. Thanks to this, an evolutionary strategy was employed to design such HIFU treatment plans. However, despite the improvements, the execution time of such a model was too prohibitive to allow routine use. In this paper, we present a new round of optimizations aimed at improving the performance of the thermal model and the evolutionary strategy. The experiments carried out in other works using an evolutionary strategy based on the island model are repeated with up to 4 times decrease in total computation time. This enables new experiments using a much higher number of sonications are introduced and evaluated. Lastly, a new real-life inspired benchmark is presented and the experiments repeated.

Published
2021
Pages
297–298
Book
GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion
ISBN
978-1-4503-8351-6
Publisher
Association for Computing Machinery
Place
Lille
DOI
EID Scopus
BibTeX
@misc{BUT175775,
  author="Jakub {Chlebík} and Jiří {Jaroš}",
  title="Distributed Evolutionary Design of HIFU Treatment Plans",
  booktitle="GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion",
  year="2021",
  pages="297--298",
  publisher="Association for Computing Machinery",
  address="Lille",
  doi="10.1145/3449726.3459550",
  isbn="978-1-4503-8351-6",
  url="https://dl.acm.org/doi/10.1145/3449726.3459550",
  note="presentation, poster"
}
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