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

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...

AI Technical Summary

Problems solved by technology

These methods require the use of ultrafast lasers or supercontinuum light sources, which may not be readily available in most cases, and require long scan times due to weak optical signals

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