Title:
CELL IMAGE ANALYSIS METHOD AND CELL ANALYSIS DEVICE
Document Type and Number:
WIPO Patent Application WO/2021/070371
Kind Code:
A1
Abstract:
One aspect of a cell analysis device according to the present invention is provided with: a holographic microscope (10); an image creation unit (23, 24) for creating a phase image of a cell on the basis of hologram data; a first learning model storage unit (251) for storing a nuclear region learning model that is created by performing machine learning through setting the phase image of the cell as an input image and using learning data in which a pseudo nuclear region image corresponding to the input image and based on a stain image obtained by staining a cell nucleus is defined as a correct image; a second learning model storage unit (261) for storing a cell region learning model that is created by performing machine learning through setting the phase image of the cell as an input image and using learning data in which a pseudo cell region image corresponding to the input image and based on the stain image obtained by staining cytoskeletons is defined as a correct image; a nuclear region inference unit (25) for obtaining, as an output image, a nuclear region inference image indicating the region of the cell nucleus by using the nuclear region learning model and setting, as an input image, the phase image created for the cell being analyzed; a cell region inference unit (26) for obtaining, as an output image, a cell region inference image indicating a cell region by using the cell region learning model and setting, as an input image, the phase image for the cell being analyzed; and a nuclear region extraction unit (27) for extracting a cell nucleus present in a range inferred to be the cell region by using the nuclear region inference image and the cell region inference image.
Inventors:
SOTOGUCHI AKIE (JP)
Application Number:
PCT/JP2019/040275
Publication Date:
April 15, 2021
Filing Date:
October 11, 2019
Export Citation:
Assignee:
SHIMADZU CORP (JP)
International Classes:
C12M1/34
Domestic Patent References:
| WO2019180833A1 | 2019-09-26 | |||
| WO2019171453A1 | 2019-09-12 | |||
| WO2016093090A1 | 2016-06-16 |
Other References:
CHRISTIANSEN, ERIC M. ET AL.: "In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images", CELL, vol. 173, 19 April 2018 (2018-04-19), pages 792 - 803, XP002788720
OUNKOMOL, CHAWIN ET AL.: "Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy", NAT. METHODS, vol. 15, no. 11, November 2018 (2018-11-01), pages 917 - 920, XP036624647, DOI: 10.1038/s41592-018-0111-2
LEE, JIMIN ET AL.: "Deep-Learning-Based Label-Free Segmentation of Cell Nuclei in Time-Lapse Refractive Index Tomograms", IEEE ACCESS, vol. 7, 21 June 2019 (2019-06-21), pages 83449 - 83460, XP011733981, DOI: 10.1109/ACCESS.2019.2924255
BIANCO, VITTORIO ET AL.: "Strategies for reducing speckle noise in digital holography", SCIENCE & APPLICATIONS, vol. 7, no. 48, 2018, pages 1 - 16, XP055816501
OUNKOMOL, CHAWIN ET AL.: "Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy", NAT. METHODS, vol. 15, no. 11, November 2018 (2018-11-01), pages 917 - 920, XP036624647, DOI: 10.1038/s41592-018-0111-2
LEE, JIMIN ET AL.: "Deep-Learning-Based Label-Free Segmentation of Cell Nuclei in Time-Lapse Refractive Index Tomograms", IEEE ACCESS, vol. 7, 21 June 2019 (2019-06-21), pages 83449 - 83460, XP011733981, DOI: 10.1109/ACCESS.2019.2924255
BIANCO, VITTORIO ET AL.: "Strategies for reducing speckle noise in digital holography", SCIENCE & APPLICATIONS, vol. 7, no. 48, 2018, pages 1 - 16, XP055816501
Attorney, Agent or Firm:
KYOTO INTERNATIONAL PATENT LAW OFFICE (JP)
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