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

Patent metadata

Field	Value
Publication number	US-2025278839-A1
Application number	US-202519205862-A
Country	US
Kind code	A1
Filing date	May 12, 2025
Priority date	Mar 30, 2018
Publication date	Sep 4, 2025
Grant date	—

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 one embodiment, 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 simplifies tissue preparation in pathology and histology fields.

First claim

What is claimed is:

1 . A system for generating digitally stained images of tissue samples, comprising:
a computing device comprising one or more processors and memory storing a neural network, wherein the neural network is trained with a plurality of chemically stained images matched with corresponding label-free images of training samples; and image processing software executed by the computing device and configured to:
receive a label-free image of a test sample obtained using fluorescence microscopy; and
generate a digitally stained image of the test sample via the neural network based on the label-free image.

2 . The system of claim 1 , wherein the digitally stained image appears substantially equivalent to a brightfield image of the test sample that has been chemically stained.

3 . The system of claim 1 , wherein the neural network comprises a plurality of neural networks.

4 . The system of claim 1 , wherein the neural network comprises a convolutional neural network.

5 . The system of claim 1 , wherein the neural network is trained using a Generative Adversarial Network (GAN) model.

6 . The system of claim 1 , wherein the test sample comprises animal tissue, plant tissue, cells, pathogens, or biological fluid smears.

7 . The system of claim 1 , wherein the image processing software generates the digitally stained image in less than one second of receiving the label-free image of the test sample.

8 . The system of claim 1 , wherein the digitally stained image comprises a microscopic image.

9 . The system of claim 1 , wherein the test sample comprises a non-fixed tissue sample.

10 . The system of claim 1 , wherein the test sample comprises a fixed tissue sample.

11 . The system of claim 10 , wherein the fixed tissue sample is embedded in paraffin.

12 . The system of claim 1 , wherein the test sample comprises a fresh tissue sample.

13 . The system of claim 1 , wherein the test sample a frozen section tissue sample.

14 . The system of claim 1 further comprising a fluorescence microscope, wherein the fluorescence microscope comprises an excitation light source that emits ultra-violet or near ultra-violet light.

15 . The system of claim 14 , wherein the fluorescence microscope comprises one or more spectral filters of a filter set.

16 . The system of claim 15 , wherein a plurality of spectral filters is used to capture a plurality of label-free images of the test sample.

17 . The system of claim 1 , wherein the image processing software is further configured to perform one or more image pre-processing operations on the label-free image.

18 . The system of claim 17 , wherein the one or more image pre-processing operations comprise at least one of contrast enhancement, contrast reversal, or image filtering.

19 . The system of claim 1 , wherein the computing device comprises one or more GPUs or ASICs for executing the neural network.

20 . The system of claim 1 , wherein the image processing software is further configured to receive at least two label-free images of the test sample and input the at least two label-free images to the neural network.

21 . The system of claim 20 , wherein the at least two label-free images are obtained using one or more wavelengths.

22 . The system of claim 20 , wherein the at least two label-free images are obtained using different resolutions.

23 . The system of claim 1 , wherein the image processing software is further configured to receive the label-free image of the test sample obtained using non-linear microscopy, holographic microscopy, Raman microscopy, or optical coherence tomography.

24 . The system of claim 1 further comprising a display device coupled to the computing device, wherein the image processing software is further configured to display the digitally stained microscopic image on the display device.

25 . The system of claim 24 , wherein the image processing software is further configured to provide a graphical user interface on the display device, the graphical user interface comprising user-selectable controls that enable a user to toggle between a plurality of different digital stains for the test sample.

Assignees

• Univ California

Inventors

• Ozcan Aydogan
• Rivenson Yair
• Wang Hongda
• Wei Zhensong

Classifications

• G06N3/0475

  Generative networks · CPC title

• G06N3/094

  Adversarial learning · CPC title

• G06N3/096

  Transfer learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title