Statistical atlases and machine learning tools applied to optimized prostate biopsy for cancer detection and estimation of volume and Gleason score

Christos Davatzikos

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

Abstract

We discuss the use of statistical atlases and machine learning tools for determining optimized biopsy procedures. Prostate cancer diagnosis most often involves the sampling of prostate tissue via placement of a number of biopsy needles in locations that are somewhat random but try to cover the gland. The purpose of this work is to establish optimal strategies for sampling the prostate tissue, using population statistics. In particular, a statistical atlas reflecting the spatial distribution of prostate cancer has been constructed via elastic registration of expert-labeled histological 3D volumes of radical prostatectomy patients[1]. This atlas reflects the probability of encountering prostate carcinoma at a given location in the gland.

Original languageEnglish (US)
Title of host publication2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI'11
Pages2107-2108
Number of pages2
DOIs
StatePublished - Nov 2 2011
Event2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States
Duration: Mar 30 2011Apr 2 2011

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
Period3/30/114/2/11

Keywords

  • prostate biopsy
  • statistical atlas of prostate cancer
  • statistical models

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Davatzikos, C. (2011). Statistical atlases and machine learning tools applied to optimized prostate biopsy for cancer detection and estimation of volume and Gleason score. In 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 (pp. 2107-2108). [5872828] (Proceedings - International Symposium on Biomedical Imaging). https://doi.org/10.1109/ISBI.2011.5872828