@inproceedings{ce66a48dafc548fbac233c44ecb19f2b,
title = "Automatic segmentation of the prostate using a genetic algorithm for prostate cancer treatment planning",
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).",
keywords = "Medical image segmentation, Radiotherapy treatment planning, Relative location, Shape, Texture",
author = "Payel Ghosh and Melanie Mitchell and Tanyi, {James A.} and Arthur, {Y. Hung}",
year = "2010",
doi = "10.1109/ICMLA.2010.115",
language = "English (US)",
isbn = "9780769543000",
series = "Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010",
pages = "752--757",
booktitle = "Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010",
note = "9th International Conference on Machine Learning and Applications, ICMLA 2010 ; Conference date: 12-12-2010 Through 14-12-2010",
}