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

Christos Davatzikos

doi:10.1109/ISBI.2011.5872828

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

Christos Davatzikos

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI'11
Pages	2107-2108
Number of pages	2
DOIs	https://doi.org/10.1109/ISBI.2011.5872828
State	Published - Nov 2 2011
Event	2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States Duration: Mar 30 2011 → Apr 2 2011

Publication series

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

Other

Other	2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Country/Territory	United States
City	Chicago, IL
Period	3/30/11 → 4/2/11

Keywords

prostate biopsy
statistical atlas of prostate cancer
statistical models

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2011.5872828

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). Article 5872828 (Proceedings - International Symposium on Biomedical Imaging). https://doi.org/10.1109/ISBI.2011.5872828

Statistical atlases and machine learning tools applied to optimized prostate biopsy for cancer detection and estimation of volume and Gleason score. / Davatzikos, Christos.
2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11. 2011. p. 2107-2108 5872828 (Proceedings - International Symposium on Biomedical Imaging).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 5872828, Proceedings - International Symposium on Biomedical Imaging, pp. 2107-2108, 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11, Chicago, IL, United States, 3/30/11. https://doi.org/10.1109/ISBI.2011.5872828

Davatzikos C. 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. 2011. p. 2107-2108. 5872828. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2011.5872828

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

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Keywords

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