SYSTEM AND METHOD FOR DETERMINING YEAST CELL VIABILITY AND CONCENTRATION

    公开(公告)号:US20210285864A1

    公开(公告)日:2021-09-16

    申请号:US16333139

    申请日:2017-09-22

    Abstract: A lens-free microscope system for automatically analyzing yeast cell viability in a stained sample includes a portable, lens-free microscopy device that includes a housing containing a light source coupled to an optical fiber, the optical fiber spaced away several centimeters from an image sensor disposed at one end of the housing, wherein the stained sample is disposed on the image sensor or a sample holder adjacent to the image sensor. Hologram images are transferred to a computing device having image processing software contained therein, the image processing software identifying yeast cell candidates of interest from back-propagated images of the stained sample, whereby a plurality of spatial features are extracted from the yeast cell candidates of interest and subject to a trained machine learning model to classify the yeast cell candidates of interest as live or dead.

    System and method for determining yeast cell viability and concentration

    公开(公告)号:US11320362B2

    公开(公告)日:2022-05-03

    申请号:US16333139

    申请日:2017-09-22

    Abstract: A lens-free microscope system for automatically analyzing yeast cell viability in a stained sample includes a portable, lens-free microscopy device that includes a housing containing a light source coupled to an optical fiber, the optical fiber spaced away several centimeters from an image sensor disposed at one end of the housing, wherein the stained sample is disposed on the image sensor or a sample holder adjacent to the image sensor. Hologram images are transferred to a computing device having image processing software contained therein, the image processing software identifying yeast cell candidates of interest from back-propagated images of the stained sample, whereby a plurality of spatial features are extracted from the yeast cell candidates of interest and subject to a trained machine learning model to classify the yeast cell candidates of interest as live or dead.

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