Learning-based prostate localization for image guided radiation therapy

Luping Zhou, Shu Liao, Wei Li, Dinggang Shen

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

6 Citations (Scopus)

Abstract

Accurate prostate localization is the key to the success of radiotherapy. It remains a difficult problem for CT images due to the low image contrast, the prostate motion, and the uncertain presence of rectum gas. In this paper, a learning based framework is proposed to improve the accuracy of prostate detection in CT. It adaptively determines distinctive feature types at distinctive image regions, thus filtering out features that are salient in image appearance, but irrelevant to prostate localization. Furthermore, an image similarity function is learned to make the image appearance distance consistent with the underlying prostate alignment. The efficacy of our proposed method has been demonstrated by the experiment.

Original languageEnglish
Title of host publication2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI'11
Pages2103-2106
Number of pages4
DOIs
Publication statusPublished - 2011
Event2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States
Duration: 2011 Mar 302011 Apr 2

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
CountryUnited States
CityChicago, IL
Period11/3/3011/4/2

Keywords

  • Feature Selection
  • IGRT
  • Image Similarity Learning
  • Prostate Localization

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Zhou, L., Liao, S., Li, W., & Shen, D. (2011). Learning-based prostate localization for image guided radiation therapy. In 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 (pp. 2103-2106). [5872827] (Proceedings - International Symposium on Biomedical Imaging). https://doi.org/10.1109/ISBI.2011.5872827