Prostate cancer diagnosis using quantitative phase imaging and machine learning algorithms

Tan H. Nguyen, Shamira Sridharan, Virgilia Macias, Andre K. Balla, Minh N. Do, Gabriel Popescu

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

Abstract

We report, for the first time, the use of Quantitative Phase Imaging (QPI) images to perform automatic prostate cancer diagnosis. A machine learning algorithm is implemented to learn textural behaviors of prostate samples imaged under QPI and produce labeled maps of different regions for testing biopsies (e.g. gland, stroma, lumen etc.). From these maps, morphological and textural features are calculated to predict outcomes of the testing samples. Current performance is reported on a dataset of more than 300 cores of various diagnosis results.

Original languageEnglish (US)
Title of host publicationQuantitative Phase Imaging
EditorsYongKeun Park, Gabriel Popescu
PublisherSPIE
ISBN (Electronic)9781628414264
DOIs
StatePublished - Jan 1 2015
Event1st Conference on Quantitative Phase Imaging, QPI 2015 - San Francisco, United States
Duration: Feb 7 2015Feb 10 2015

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9336
ISSN (Print)1605-7422

Other

Other1st Conference on Quantitative Phase Imaging, QPI 2015
CountryUnited States
CitySan Francisco
Period2/7/152/10/15

Keywords

  • Quantitative Phase Imaging
  • automatic diagnosis
  • prostate cancer
  • texton analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Nguyen, T. H., Sridharan, S., Macias, V., Balla, A. K., Do, M. N., & Popescu, G. (2015). Prostate cancer diagnosis using quantitative phase imaging and machine learning algorithms. In Y. Park, & G. Popescu (Eds.), Quantitative Phase Imaging [933619] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9336). SPIE. https://doi.org/10.1117/12.2080321