Automatic segmentation of the prostate using a genetic algorithm for prostate cancer treatment planning

Payel Ghosh, Melanie Mitchell, James A. Tanyi, Y. Hung Arthur

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

2 Scopus citations

Abstract

This paper presents a genetic algorithm (GA) for combining representations of learned priors such as shape, regional properties and relative location of organs into a single framework in order to perform automated segmentation of the prostate. Prostate segmentation is typically performed manually by an expert physician and is used to determine the locations for radioactive seed placement during radiotherapy treatment planning. The GA accounts for the uncertainty in the definitions of tumor margins by combining known representations of shape, texture and relative location of organs to perform automatic segmentation in two (2D) as well as three dimensions (3D).

Original languageEnglish (US)
Title of host publicationProceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
Pages752-757
Number of pages6
DOIs
StatePublished - Dec 1 2010
Event9th International Conference on Machine Learning and Applications, ICMLA 2010 - Washington, DC, United States
Duration: Dec 12 2010Dec 14 2010

Publication series

NameProceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010

Other

Other9th International Conference on Machine Learning and Applications, ICMLA 2010
CountryUnited States
CityWashington, DC
Period12/12/1012/14/10

Keywords

  • Medical image segmentation
  • Radiotherapy treatment planning
  • Relative location
  • Shape
  • Texture

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

Fingerprint Dive into the research topics of 'Automatic segmentation of the prostate using a genetic algorithm for prostate cancer treatment planning'. Together they form a unique fingerprint.

  • Cite this

    Ghosh, P., Mitchell, M., Tanyi, J. A., & Arthur, Y. H. (2010). Automatic segmentation of the prostate using a genetic algorithm for prostate cancer treatment planning. In Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010 (pp. 752-757). [5708937] (Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010). https://doi.org/10.1109/ICMLA.2010.115