Method and system for digital staining of label-free fluorescence images using deep learning

A fluorescent image, unlabeled technology, applied in neural learning methods, medical images, image data processing, etc., can solve the problems of weak optical signal, difficult to use, long scanning time, etc., and achieve the effect of saving time

Pending Publication Date: 2020-12-18
RGT UNIV OF CALIFORNIA
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  • Application Information

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These methods require the use of ultrafast lasers or supercontinuum light sources, which ma...

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  • Method and system for digital staining of label-free fluorescence images using deep learning
  • Method and system for digital staining of label-free fluorescence images using deep learning
  • Method and system for digital staining of label-free fluorescence images using deep learning

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Embodiment Construction

[0029] figure 1 One embodiment of a system 2 for outputting a digitally stained image 40 from an input microscope image 20 of a sample 22 is schematically shown. As described herein, the input image 20 is a fluorescent image 20 of a sample 22 (such as tissue in one embodiment) that has not been stained or labeled with a fluorescent stain or marker. That is, the input image 20 is an autofluorescent image 20 of a sample 22 that fluoresces as a result of one or more endogenous fluorescence or other endogenous emitters of frequency-shifted light contained therein. Frequency-shifted light is light emitted at a frequency (or wavelength) different from the incident frequency (or wavelength). Endogenous fluorescence or endogenous emitters of frequency-shifted light can include molecules, compounds, complexes, molecular species, biomolecules, pigments, tissues, and the like. In some embodiments, the input image 20 (eg, raw fluorescence image) is subjected to one or more linear or non...

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Abstract

A deep learning-based digital staining method and system are disclosed that enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples based on autofluorescence images acquired using a fluorescent microscope. The system and method have particular applicability for the creation of digitally/virtually-stained whole slide images (WSIs) of unlabeled/unstained tissue samples that are analyzes by a histopathologist. The methods bypass the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses, in oneembodiment, a convolutional neural network trained using a generative adversarial network model to transform fluorescence images of an unlabeled sample into an image that is equivalent to the brightfield image of the chemically stained-version of the same sample. This label-free digital staining method eliminates cumbersome and costly histochemical staining procedures and significantly simplifiestissue preparation in pathology and histology fields.

Description

[0001] Cross References to Related Applications [0002] This application claims priority to U.S. Provisional Patent Application No. 62 / 651,005, filed March 30, 2018, which is hereby incorporated by reference. Claim priority under 35 U.S.C. §119 and any other applicable statute. technical field [0003] The technical field generally relates to methods and systems for imaging unstained (ie, unlabeled) tissue. In particular, the technical field relates to microscopy methods and systems for digitally or virtually staining images of unstained or unlabeled tissue using deep neural network learning. Deep learning in neural networks (a class of machine learning algorithms) is used to digitally stain an image of an unlabeled tissue section into an image equivalent to a stained or labeled microscope image of the same sample. Background technique [0004] Microscopic imaging of tissue samples is an essential tool for the diagnosis of various diseases and forms a major tool in pathol...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06T1/00G06T1/20G06T1/40G06T7/00G06T7/10G06T7/11G06V10/764G06V10/82
CPCG06T7/11G06T2207/10056G06T2207/10024G06T2207/30024G06T2207/20081G06T2207/20084G06V2201/03G06V10/82G06V10/764G06N3/08G06V10/10G06F18/24G06F18/2155G16H30/20G16H30/40G16H70/60
Inventor 阿伊多根·奥兹坎亚伊尔·里文森王宏达魏赈嵩
Owner RGT UNIV OF CALIFORNIA
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