Imaging system for detecting intraoperative contrast agent in tissue

Abstract

Describes systems and methods for providing quantitative measurements of the distribution of a contrast agent in a tissue sample by identifying the cellular distribution of the contrast agent using a combination of high-resolution optical sectioning imaging modalities. In some cases, the systems and methods can be used in an intraoperative setting to guide biopsies or surgeries.

Description

[0001] Cross-reference

[0002] This application claims the benefit of U.S. Provisional Application No. 62 / 833,553, filed on April 12, 2019, which is hereby incorporated by reference in its entirety. SUMMARY OF THE INVENTION

[0003] The present disclosure relates to a system for imaging a tissue sample, comprising: a) a first optical subsystem configured to acquire a high-resolution image of the distribution of a cell-associated contrast agent within the tissue sample using a first optical sectioning imaging mode; b) a second optical subsystem configured to acquire a high-resolution image of the morphology of the tissue sample using a second optical sectioning imaging mode, wherein the first optical subsystem and the second optical subsystem are configured to image the same optical plane within the tissue sample; c) a processor configured to run an image interpretation algorithm that processes an image acquired using one or both of the first optical sectioning imaging mode and the second optical sectioning imaging mode to identify individual cells and determine the locations of the individual cells, and outputs a quantitative measurement of the signal from the cell-associated contrast agent by measuring the signal at a location corresponding to the location of the individual cell in the image acquired using the first imaging mode. In some embodiments, the first optical sectioning imaging mode includes two-photon fluorescence microscopy, confocal fluorescence microscopy, light-sheet microscopy, or structured illumination microscopy. In some embodiments, the second optical sectioning imaging mode includes stimulated Raman scattering microscopy, coherent anti-Stokes Raman scattering microscopy, confocal reflection microscopy, second harmonic generation microscopy, or third harmonic generation microscopy. In some embodiments, the first optical subsystem and the second optical subsystem are configured to have an axial resolution of less than 10 μm. In some embodiments, the first optical subsystem and the second optical subsystem are configured to have a lateral resolution of less than 5 μm. In some embodiments, the first optical subsystem is further configured to acquire high-resolution images in two or more detection wavelength ranges. In some embodiments, a first detection wavelength range of the two or more detection wavelength ranges includes an emission peak of the cell-associated contrast agent; a second detection wavelength range of the two or more detection wavelength ranges does not include the emission peak of the cell-associated contrast agent; and the image interpretation algorithm further processes the image acquired using the first detection wavelength to identify individual cells and determine the locations of the individual cells, and outputs a quantitative measurement of the signal from the cell-associated contrast agent by measuring the signal at the location of the individual cell and correcting the signal by a background value, the background value being measured at a location corresponding to the location of the individual cell in the image acquired using the second detection wavelength range. In some embodiments, the second optical sectioning imaging mode includes stimulated Raman scattering microscopy and acquires the image at a wavenumber corresponding to the CH2-vibration of lipid molecules at 2,850 cm -1 -1. In some embodiments, the image is also acquired at a wavenumber corresponding to the CH3-vibration of protein and nucleic acid molecules at 2,930 cm -1Obtain an image at the wavenumber. In some embodiments, the signal obtained from the cell-associated contrast agent includes a fluorescence signal, a phosphorescence signal, or any combination thereof. In some embodiments, the cell-associated contrast agent comprises an antibody conjugated to a fluorophore, a quantum dot, a nanoparticle, or a phosphor. In some embodiments, the cell-associated contrast agent comprises a fluorescent enzyme substrate. In some embodiments, the cell-associated contrast agent comprises fluorescein, 5-ALA, BLZ-100, or LUM015. In some embodiments, the cell-associated contrast agent includes 5-aminolevulinic acid (5-ALA), wherein the first emission wavelength range includes light at 640 nm and the second emission wavelength includes a wavelength shorter than 600 nm. In some embodiments, the image interpretation algorithm includes a Canny edge detection algorithm, a Canny-Deriche edge detection algorithm, a first-order gradient edge detection algorithm, a second-order differential edge detection algorithm, a phase coherence edge detection algorithm, an image segmentation algorithm, and an intensity threshold algorithm, an intensity clustering algorithm, an intensity histogram-based algorithm, a feature recognition algorithm, a pattern recognition algorithm, a generalized Hough transform algorithm, a circular Hough transform algorithm, a Fourier transform algorithm, a fast Fourier transform algorithm, a wavelet analysis algorithm, an autocorrelation algorithm, or any combination thereof. In some embodiments, the image interpretation algorithm detects individual cells based on the feature size, shape, pattern, intensity, or any combination thereof of the image. In some embodiments, the image interpretation algorithm includes an artificial intelligence or machine learning algorithm. In some embodiments, the image interpretation algorithm includes a supervised machine learning algorithm, an unsupervised machine learning algorithm, a semi-supervised machine learning algorithm, or any combination thereof. In some embodiments, the machine learning algorithm includes an artificial neural network algorithm, a deep convolutional neural network algorithm, a deep recurrent neural network, a generative adversarial network, a support vector machine, a hierarchical clustering algorithm, a Gaussian process regression algorithm, a decision tree algorithm, a logical model tree algorithm, a random forest algorithm, a fuzzy classifier algorithm, a k-means algorithm, an expectation maximization algorithm, a fuzzy clustering algorithm, or any combination thereof. In some embodiments, the machine learning algorithm is trained using a training data set, the training data set including imaging data obtained for archived histopathological tissue samples, imaging data obtained for fresh histopathological tissue samples, or any combination thereof. In some embodiments, the training data set is continuously, periodically, or randomly updated using imaging data obtained by two or more systems deployed at the same or different sites. In some embodiments, the two or more systems are deployed at different sites, and the training data set is continuously, periodically, or randomly updated via a network connection. In some embodiments, the image interpretation algorithm determines the total intensity or average intensity of the signal obtained from the cell-associated contrast agent. In some embodiments, the image interpretation algorithm determines whether the signal obtained from the cell-associated contrast agent for an individual cell is higher than a specified threshold level for positive control cells.In some embodiments, the image interpretation algorithm outputs the total number of contrast-positive cells within the image of the tissue sample, the density of contrast-positive cells within the image of the tissue sample, the percentage of contrast-positive cells within the image of the tissue sample, or any combination thereof. In some embodiments, the image interpretation algorithm also outputs a cellularity fraction based on the image obtained using the second optical sectioning imaging mode. In some embodiments, an image of the tissue sample is acquired in vivo. In some embodiments, an image of the tissue sample is acquired in vitro. In some embodiments, the system is used during surgery to identify a location for performing a biopsy or to determine whether an excision is complete.

[0004] The present disclosure also discloses a method for cell-resolution imaging of tissue samples, the method comprising: a) obtaining a high-resolution optical section image of the distribution of cell-associated contrast agent within the tissue sample using a first imaging modality; b) obtaining a high-resolution optical section image of the tissue sample morphology that is optically in the same focal plane as in (a) within the tissue sample using a second imaging modality; and c) processing the images obtained using one or both of the first imaging modality and the second imaging modality using an image interpretation algorithm that identifies individual cells and determines the positions of the individual cells, and outputs a quantitative measurement of the signal obtained from the cell-associated contrast agent at cell resolution by measuring the signal at positions corresponding to the positions of the individual cells in the image obtained using the first imaging modality. In some embodiments, the first imaging modality includes two-photon fluorescence microscopy, confocal fluorescence microscopy, light-sheet microscopy, or structured illumination microscopy. In some embodiments, the second imaging modality includes stimulated Raman scattering microscopy, coherent anti-Stokes Raman scattering microscopy, confocal reflection microscopy, second harmonic generation microscopy, or third harmonic generation microscopy. In some embodiments, the images obtained using the first imaging modality and the second imaging modality have an axial resolution of less than 10 μm. In some embodiments, the images obtained using the first imaging modality and the second imaging modality have a lateral resolution of less than 5 μm. In some embodiments, the method further includes obtaining images using the first imaging modality in two or more detection wavelength ranges. In some embodiments, a first detection wavelength range of the two or more detection wavelength ranges includes an emission peak of the cell-associated contrast agent; a second detection wavelength range of the two or more detection wavelength ranges does not include the emission peak of the cell-associated contrast agent; and the image interpretation algorithm further processes the image obtained using the first detection wavelength to identify individual cells and determine the positions of the individual cells, and outputs a quantitative measurement of the signal obtained from the cell-associated contrast agent by measuring the signal at the positions of the individual cells and correcting the signal by a background value, the background value being measured at positions corresponding to the positions of the individual cells in the image obtained using the second emission wavelength range. In some embodiments, the second imaging modality includes stimulated Raman scattering microscopy and the image is obtained at a wavenumber corresponding to the CH2-vibration of lipid molecules at 2,850 cm -1 In some embodiments, the image is also obtained at a wavenumber corresponding to the CH3-vibration of protein and nucleic acid molecules at 2,930 cm -1Obtain an image at the wavenumber. In some embodiments, the signal obtained from the cell-associated contrast agent includes a fluorescence signal, a phosphorescence signal, or any combination thereof. In some embodiments, the cell-associated contrast agent comprises an antibody conjugated to a fluorophore, a quantum dot, a nanoparticle, or a phosphor. In some embodiments, the cell-associated contrast agent comprises a fluorescent enzyme substrate. In some embodiments, the cell-associated contrast agent comprises fluorescein, 5-ALA, BLZ-100, or LUM015. In some embodiments, the cell-associated contrast agent includes 5-aminolevulinic acid (5-ALA), wherein the first emission wavelength range includes light at 640 nm and the second emission wavelength includes a wavelength shorter than 600 nm. In some embodiments, the image interpretation algorithm includes a Canny edge detection algorithm, a Canny-Deriche edge detection algorithm, a first-order gradient edge detection algorithm, a second-order differential edge detection algorithm, a phase coherence edge detection algorithm, an image segmentation algorithm, and an intensity threshold algorithm, an intensity clustering algorithm, an intensity histogram-based algorithm, a feature recognition algorithm, a pattern recognition algorithm, a generalized Hough transform algorithm, a circular Hough transform algorithm, a Fourier transform algorithm, a fast Fourier transform algorithm, a wavelet analysis algorithm, an autocorrelation algorithm, or any combination thereof. In some embodiments, the image interpretation algorithm detects individual cells based on the feature size, shape, pattern, intensity, or any combination thereof of the image. In some embodiments, the image interpretation algorithm includes an artificial intelligence or machine learning algorithm. In some embodiments, the image interpretation algorithm includes a supervised machine learning algorithm, an unsupervised machine learning algorithm, a semi-supervised machine learning algorithm, or any combination thereof. In some embodiments, the machine learning algorithm includes an artificial neural network algorithm, a deep convolutional neural network algorithm, a deep recurrent neural network, a generative adversarial network, a support vector machine, a hierarchical clustering algorithm, a Gaussian process regression algorithm, a decision tree algorithm, a logical model tree algorithm, a random forest algorithm, a fuzzy classifier algorithm, a k-means algorithm, an expectation maximization algorithm, a fuzzy clustering algorithm, or any combination thereof. In some embodiments, the machine learning algorithm is trained using a training data set that includes imaging data obtained for archived histopathology tissue samples, imaging data obtained for fresh histopathology tissue samples, or any combination thereof. In some embodiments, the training data set is continuously, periodically, or randomly updated using imaging data obtained by two or more systems deployed at the same or different sites. In some embodiments, the two or more systems are deployed at different sites, and the training data set is continuously, periodically, or randomly updated via a network connection. In some embodiments, the image interpretation algorithm determines the total intensity or average intensity of the signal obtained from the cell-associated contrast agent. In some embodiments, the image interpretation algorithm determines whether the signal obtained from the cell-associated contrast agent for an individual cell is higher than a specified threshold level for contrast-positive cells.In some embodiments, the image interpretation algorithm outputs the total number of contrast-positive cells, the density of contrast-positive cells, the percentage of contrast-positive cells, or any combination thereof, within the image of the tissue sample. In some embodiments, the image interpretation algorithm also outputs a cellularity fraction based on the image obtained using the second optical sectioning imaging mode. In some embodiments, an image of the tissue sample is acquired in vivo. In some embodiments, an image of the tissue sample is acquired in vitro. In some embodiments, the method is used during surgery to identify a location for performing a biopsy or to determine whether an excision is complete.

[0005] The present disclosure relates to a system for imaging a tissue sample, the system comprising: a) a high-resolution optical sectioning microscope configured to acquire an image of the tissue sample; and b) a processor configured to run an image interpretation algorithm that detects individual cells in the image acquired by the high-resolution optical sectioning microscope and outputs a quantitative measurement of a signal obtained from a cell-associated contrast agent. In some embodiments, the high-resolution optical sectioning microscope comprises a two-photon fluorescence microscope, a confocal fluorescence microscope, a light-sheet microscope, or a structured illumination microscope. In some embodiments, the high-resolution optical sectioning microscope has an axial resolution of less than 10 μm. In some embodiments, the high-resolution optical sectioning microscope has a lateral resolution of less than 5 μm. In some embodiments, the high-resolution optical sectioning microscope is configured to acquire images in two or more emission wavelength ranges. In some embodiments, a first emission wavelength range of the two or more detection wavelength ranges includes an emission peak of the cell-associated contrast agent; a second emission wavelength range of the two or more detection wavelength ranges does not include the emission peak of the cell-associated contrast agent; and the image interpretation algorithm processes the image acquired using the first detection wavelength range to identify individual cells and determine the positions of the individual cells, and outputs a quantitative measurement of the signal obtained from the cell-associated contrast agent by measuring the signal at the positions of the individual cells and correcting the signal using a background value, the background value being measured at corresponding positions in the image acquired using the second emission wavelength range. In some embodiments, the signal obtained from the cell-associated contrast agent includes a fluorescence signal, a phosphorescence signal, or any combination thereof. In some embodiments, the cell-associated contrast agent comprises an antibody conjugated to a fluorophore, a quantum dot, a nanoparticle, or a phosphor. In some embodiments, the cell-associated contrast agent comprises a fluorescent enzyme substrate. In some embodiments, the cell-associated contrast agent comprises fluorescein, 5-ALA, BLZ-100, or LUM015. In some embodiments, the cell-associated contrast agent comprises 5-aminolevulinic acid (5-ALA), wherein the first emission wavelength range includes light at 640 nm and the second emission wavelength includes wavelengths shorter than 600 nm. In some embodiments, the image interpretation algorithm comprises a Canny edge detection algorithm, a Canny-Deriche edge detection algorithm, a first-order gradient edge detection algorithm, a second-order differential edge detection algorithm, a phase coherence edge detection algorithm, an image segmentation algorithm, and an intensity threshold algorithm, an intensity clustering algorithm, an intensity histogram-based algorithm, a feature recognition algorithm, a pattern recognition algorithm, a generalized Hough transform algorithm, a circular Hough transform algorithm, a Fourier transform algorithm, a fast Fourier transform algorithm, a wavelet analysis algorithm, an autocorrelation algorithm, or any combination thereof.In some embodiments, the image interpretation algorithm detects individual cells based on image feature size, shape, pattern, intensity, or any combination thereof. In some embodiments, the image interpretation algorithm includes an artificial intelligence or machine learning algorithm. In some embodiments, the image interpretation algorithm includes a supervised machine learning algorithm, an unsupervised machine learning algorithm, a semi-supervised machine learning algorithm, or any combination thereof. In some embodiments, the machine learning algorithm includes an artificial neural network algorithm, network, support vector machine, hierarchical clustering algorithm, Gaussian process regression algorithm, decision tree algorithm, logical model tree algorithm, random forest algorithm, fuzzy classifier algorithm, k-means algorithm, expectation maximization algorithm, fuzzy clustering algorithm, or any combination thereof. In some embodiments, the machine learning algorithm is trained using a training data set that includes imaging data obtained for archived histopathology tissue samples, imaging data obtained for fresh histopathology tissue samples, or any combination thereof. In some embodiments, the training data set is continuously, periodically, or randomly updated using imaging data obtained by two or more systems deployed at the same or different sites. In some embodiments, the two or more systems are deployed at different sites, and the training data set is continuously, periodically, or randomly updated via a network connection. In some embodiments, the quantitative measurement of the signal obtained from the cell-associated contrast agent includes a measurement of the amount of contrast agent associated with one or more individual cells in the image. In some embodiments, the quantitative measurement of the signal obtained from the cell-associated contrast agent includes a measurement of the total number of contrast-positive cells within the image, the density of contrast-positive cells within the image, the percentage of contrast-positive cells within the image, or any combination thereof. In some embodiments, an image of the tissue sample is acquired in vivo. In some embodiments, an image of the tissue sample is acquired in vitro. In some embodiments, the system is used during surgery to identify locations for performing a biopsy or to determine whether an excision is complete.

[0006] The present disclosure relates to a method for imaging a tissue sample, the method comprising: a) obtaining a high-resolution optical section image of the tissue sample; and b) processing the image using an image interpretation algorithm that detects individual cells in the image and outputs a quantitative measurement of a signal obtained from a cell-associated contrast agent at the location of one or more individual cells. In some embodiments, the first high-resolution optical section image is obtained using two-photon fluorescence microscopy, confocal fluorescence microscopy, light-sheet microscopy, or structured illumination microscopy. In some embodiments, the first imaging modality comprises two-photon fluorescence microscopy, confocal fluorescence microscopy, light-sheet microscopy, or structured illumination microscopy. In some embodiments, the obtained image has an axial resolution of less than 10 μm. In some embodiments, the obtained image has a lateral resolution of less than 5 μm. In some embodiments, the signal obtained from the cell-associated contrast agent comprises a fluorescence signal, a phosphorescence signal, or any combination thereof. In some embodiments, the cell-associated contrast agent comprises an antibody conjugated to a fluorophore, a quantum dot, a nanoparticle, or a phosphor. In some embodiments, the cell-associated contrast agent comprises a fluorescent enzyme substrate. In some embodiments, the cell-associated contrast agent comprises fluorescein, 5-ALA, BLZ-100, or LUM015. In some embodiments, the image interpretation algorithm comprises a Canny edge detection algorithm, a Canny-Deriche edge detection algorithm, a first-order gradient edge detection algorithm, a second-order differential edge detection algorithm, a phase coherence edge detection algorithm, an image segmentation algorithm, and an intensity threshold algorithm, an intensity clustering algorithm, an intensity histogram-based algorithm, a feature recognition algorithm, a pattern recognition algorithm, a generalized Hough transform algorithm, a circular Hough transform algorithm, a Fourier transform algorithm, a fast Fourier transform algorithm, a wavelet analysis algorithm, an autocorrelation algorithm, or any combination thereof. In some embodiments, the image interpretation algorithm detects individual cells based on image feature size, shape, pattern, intensity, or any combination thereof. In some embodiments, the image interpretation algorithm comprises an artificial intelligence or machine learning algorithm. In some embodiments, the image interpretation algorithm comprises a supervised machine learning algorithm, an unsupervised machine learning algorithm, a semi-supervised machine learning algorithm, or any combination thereof. In some embodiments, the machine learning algorithm comprises an artificial neural network algorithm, a deep convolutional neural network algorithm, a deep recurrent neural network, a generative adversarial network, a support vector machine, a hierarchical clustering algorithm, a Gaussian process regression algorithm, a decision tree algorithm, a logical model tree algorithm, a random forest algorithm, a fuzzy classifier algorithm, a k-means algorithm, an expectation maximization algorithm, a fuzzy clustering algorithm, or any combination thereof. In some embodiments, the machine learning algorithm is trained using a training data set that comprises imaging data obtained for archived histopathological tissue samples, imaging data obtained for fresh histopathological tissue samples, or any combination thereof.In some embodiments, the training dataset is updated continuously, periodically, or randomly using imaging data obtained from two or more systems that have been deployed at the same or different sites. In some embodiments, the two or more systems are deployed at different sites, and the training dataset is updated continuously, periodically, or randomly via a network connection. In some embodiments, the image interpretation algorithm determines the average intensity of the signal obtained from the cell-associated contrast agent. In some embodiments, the image interpretation algorithm determines whether the signal in each individual cell is above a specified threshold level for contrast-positive cells. In some embodiments, the image interpretation algorithm outputs the total number of contrast-positive cells in the image, the density of contrast-positive cells in the image, the percentage of contrast-positive cells in the image, or any combination thereof. In some embodiments, the image interpretation algorithm outputs a cellularity score. In some embodiments, an image of a tissue sample is acquired in vivo. In some embodiments, an image of a tissue sample is acquired in vitro. In some embodiments, the system is used during surgery to identify locations for performing a biopsy or to determine whether an excision is complete.

[0007] Disclosed herein are methods for detecting cell-associated optical contrast agents in tissue samples, the methods comprising: a) acquiring a first high-resolution optical section image of the tissue sample at a first emission wavelength range that includes an emission peak of the cell-associated contrast agent, b) acquiring a second high-resolution optical section image of the tissue sample at a second emission wavelength range that does not include the emission peak of the cell-associated contrast agent; and c) applying a pseudocolor algorithm to the first and second images to generate a multicolored image of the tissue sample that facilitates human interpretation. In some embodiments, the first high-resolution optical section image and the second high-resolution optical section image are acquired using two-photon fluorescence microscopy, confocal fluorescence microscopy, light sheet microscopy, or structured illumination microscopy. In some embodiments, the image acquired using the first emission wavelength range is further processed by an image interpretation algorithm to identify individual cells and the locations of the individual cells, and to output the total number of contrast-positive cells in the image, the density of contrast-positive cells in the image, the percentage of contrast-positive cells in the image, or any combination thereof. In some embodiments, an image of a tissue sample is acquired in vivo. In some embodiments, an image of a tissue sample is acquired in vitro. In some embodiments, the method is used during surgery to identify locations for performing a biopsy or to determine whether an excision is complete.

[0008] The present disclosure also discloses a method for guiding a surgical resection, the method comprising: a) obtaining a high-resolution optical section image of the distribution of a cell-associated contrast agent within the tissue sample using a first imaging modality; b) obtaining a high-resolution optical section image of the tissue sample morphology at the same optical focal plane of the tissue sample using a second imaging modality; and c) processing the images obtained using one or both of the first imaging modality and the second imaging modality using an image interpretation algorithm, the image interpretation algorithm identifying individual cells and determining the positions of the individual cells, and outputting a quantitative measurement of the signal from the cell-associated contrast agent at the cell resolution by measuring the signal at a position corresponding to the position of the individual cell in the image obtained using the first imaging modality.

[0009] The present disclosure discloses a method for guiding a surgical resection, the method comprising: a) obtaining a high-resolution optical section image of a tissue sample; and b) processing the image using an image interpretation algorithm, the image interpretation algorithm detecting individual cells in the image and outputting a quantitative measurement of the signal from a cell-associated contrast agent at the position of one or more individual cells.

[0010] The present disclosure discloses a method for guiding a surgical resection, the method comprising: a) obtaining a first high-resolution optical section image of the tissue sample at a first emission wavelength range, the first emission wavelength range including an emission peak of a cell-associated contrast agent, b) obtaining a second high-resolution optical section image of the tissue sample at a second emission wavelength range, the second emission wavelength range not including the emission peak of the cell-associated contrast agent; and c) applying a pseudocolor algorithm to the first image and the second image to generate a multicolored image of the tissue sample, the multicolored image facilitating human interpretation.

[0011] For any method disclosed herein, in some embodiments, images of at least one imaging modality are acquired using two-photon fluorescence microscopy, confocal fluorescence microscopy, light-sheet microscopy, or structured illumination microscopy. In some embodiments, images acquired using at least a second imaging modality are acquired using stimulated Raman scattering microscopy, coherent anti-Stokes Raman scattering microscopy, confocal reflectance microscopy, second harmonic generation microscopy, or third harmonic generation microscopy. In some embodiments, the tissue sample is a brain tissue sample, a breast tissue sample, a lung tissue sample, a pancreatic tissue sample, or a prostate tissue sample. In some embodiments, images of the tissue sample are acquired in vivo. In some embodiments, images of the tissue sample are acquired in vitro. In some embodiments, an image interpretation algorithm for processing images acquired using a first imaging modality or a second imaging modality outputs the total number of contrast-positive cells within the image, the density of contrast-positive cells within the image, the percentage of contrast-positive cells within the image, or any combination thereof. In some embodiments, an image interpretation algorithm for processing images acquired using a first imaging modality or a second imaging modality includes a machine learning algorithm. In some embodiments, the machine learning algorithm includes a supervised machine learning algorithm, an unsupervised machine learning algorithm, a semi-supervised machine learning algorithm, or any combination thereof. In some embodiments, the machine learning algorithm includes an artificial neural network algorithm, a deep convolutional neural network algorithm, a deep recurrent neural network, a generative adversarial network, a support vector machine, a hierarchical clustering algorithm, a Gaussian process regression algorithm, a decision tree algorithm, a logical model tree algorithm, a random forest algorithm, a fuzzy classifier algorithm, a k-means algorithm, an expectation maximization algorithm, a fuzzy clustering algorithm, or any combination thereof. In some embodiments, the machine learning algorithm is trained using a training data set that includes imaging data acquired for archived histopathological tissue samples, imaging data acquired for fresh histopathological tissue samples, or any combination thereof. In some embodiments, the training data set is continuously, periodically, or randomly updated using imaging data acquired by two or more systems that have been deployed at the same or different sites. In some embodiments, the two or more systems are deployed at different sites, and the training data set is continuously, periodically, or randomly updated via a network connection.

[0012] The present disclosure relates to a method for guiding a surgical resection, the method comprising: a) obtaining a high-resolution optical section image of the distribution of a cell-associated contrast agent within a tissue sample using two-photon fluorescence microscopy; b) obtaining a high-resolution optical section image of the morphology of the tissue sample within the same optical focal plane of the tissue sample using stimulated Raman scattering microscopy; and c) processing the image obtained using stimulated Raman scattering using an image interpretation algorithm that identifies individual cells and determines the positions of the individual cells, and outputs a quantitative measurement of the signal obtained from the cell-associated contrast agent at the cell resolution by measuring the signal at positions corresponding to the positions of the individual cells in the image obtained using two-photon fluorescence.

[0013] The present disclosure relates to a method for guiding a surgical resection, the method comprising: a) obtaining a high-resolution optical section image of a tissue sample using two-photon fluorescence; and b) processing the image using an image interpretation algorithm that detects individual cells in the image and outputs a quantitative measurement of the signal obtained from a cell-associated contrast agent at one or more individual cell positions at the cell resolution.

[0014] The present disclosure relates to a method for guiding a surgical resection, the method comprising: a) obtaining a first high-resolution optical section two-photon fluorescence image of the tissue sample at a first emission wavelength range that includes an emission peak of a cell-associated contrast agent, b) obtaining a second high-resolution optical section two-photon fluorescence image of the tissue sample at a second emission wavelength range that does not include the emission peak of the cell-associated contrast agent; and c) applying a pseudocolor algorithm to the first two-photon fluorescence image and the second two-photon fluorescence image to generate a multicolored image of the tissue sample, the multicolored image facilitating human interpretation.

[0015] In any of the methods disclosed herein, in some embodiments, images of at least one imaging modality are acquired using confocal fluorescence microscopy, light sheet microscopy, or structured illumination microscopy instead of two-photon fluorescence microscopy. In some embodiments, images acquired using at least a second imaging modality are acquired using coherent anti-Stokes Raman scattering microscopy, confocal reflection microscopy, second harmonic generation microscopy, or third harmonic generation microscopy instead of stimulated Raman scattering microscopy. In some embodiments, the tissue sample is a brain tissue sample, a breast tissue sample, a lung tissue sample, a pancreatic tissue sample, or a prostate tissue sample. In some embodiments, images of the tissue sample are acquired in vivo. In some embodiments, images of the tissue sample are acquired in vitro. In some embodiments, an image interpretation algorithm for processing images acquired using two-photon fluorescence outputs the total number of contrast-positive cells in the image, the density of contrast-positive cells in the image, the percentage of contrast-positive cells in the image, or any combination thereof. In some embodiments, an image interpretation algorithm for processing images acquired using two-photon fluorescence or stimulated Raman scattering includes a machine learning algorithm. In some embodiments, the machine learning algorithm includes a supervised machine learning algorithm, an unsupervised machine learning algorithm, a semi-supervised machine learning algorithm, or any combination thereof. In some embodiments, the machine learning algorithm includes an artificial neural network algorithm, a deep convolutional neural network algorithm, a deep recurrent neural network, a generative adversarial network, a support vector machine, a hierarchical clustering algorithm, a Gaussian process regression algorithm, a decision tree algorithm, a logical model tree algorithm, a random forest algorithm, a fuzzy classifier algorithm, a k-means algorithm, an expectation maximization algorithm, a fuzzy clustering algorithm, or any combination thereof. In some embodiments, the machine learning algorithm is trained using a training data set that includes imaging data acquired for archived histopathological tissue samples, imaging data acquired for fresh histopathological tissue samples, or any combination thereof. In some embodiments, the training data set is continuously, periodically, or randomly updated using imaging data acquired by two or more systems deployed at the same or different sites. In some embodiments, the two or more systems are deployed at different sites, and the training data set is continuously, periodically, or randomly updated via a network connection.

[0016] Incorporated by reference

[0017] All publications, patents, and patent applications mentioned in this specification are hereby incorporated by reference in their entirety to the same extent as if each individual publication, patent, or patent application were specifically and individually indicated to be incorporated by reference in its entirety. If there is a conflict between the terms in this document and those in the incorporated references, the terms in this document shall prevail. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the invention will be obtained by reference to the following detailed description of illustrative embodiments that make use of the principles of the invention and the accompanying drawings, in which:

[0019] Figure 1A Non-limiting examples of visible light images of brain tissue during surgery to remove gliomas are provided.

[0020] Figure 1B There are provided Figure 1A Non-limiting examples of fluorescence images of brain tissue shown in, where 5-aminolevulinic acid (5-ALA) HCl has been used as a contrast agent to better visualize and guide the removal of gliomas.

[0021] Figure 2 A schematic diagram of a high-resolution optical microscope is provided that combines stimulated Raman scattering (SRS) microscopy, which images tissue morphology, with two-photon fluorescence (2P) microscopy, which images the distribution of fluorescent contrast agents.

[0022] Figure 3A Non-limiting examples of two-photon fluorescence images of brain tumor tissue samples administered 5-ALA as a contrast agent are provided.

[0023] Figure 3B There are provided Figure 3A Non-limiting examples of two-photon fluorescence images of brain tumor tissue samples shown in after processing the images to identify individual cells in the images.

[0024] Figure 4A Non-limiting examples of ex vivo two-photon fluorescence images of biopsy tissue from patients administered 5-ALA as a contrast agent are provided.

[0025] Figure 4B Non-limiting examples of ex vivo two-photon fluorescence images of biopsy tissue from patients not administered a contrast agent are provided.

[0026] Figure 5 Non-limiting examples of two-photon fluorescence emission spectra of brain tissue samples administered 5-ALA as a contrast agent are provided.

[0027] Figure 6 Non-limiting examples of single-photon fluorescence emission spectra (left figure) obtained from tissue samples (right figure) administered 5-ALA are provided.

[0028] Figure 7 Schematic diagrams of energy level diagrams and ground state to vibrational or electronic state transitions that occur during stimulated Raman and two-photon fluorescence processes are provided.

[0029] Figure 8A Provides a non-limiting example of a stimulated Raman scattering image of a brain cancer tissue sample.

[0030] Figure 8B Provides a non-limiting example of a two-photon fluorescence image that shows the distribution of a contrast agent (5-ALA) in the same brain cancer tissue sample described in Figure 8A .

[0031] Figure 9A Shows the Figure 8A automatic detection of cell nuclei or cells (circles) in a stimulated Raman scattering image.

[0032] Figure 9B Shows the Figure 8B automatic measurement of fluorescence intensity within a specific cell region (circle) centered on the cell nuclei or cells detected in the image shown in

[0033] Figure 10A Provides a first view of a dual-channel, non-descanned detector design that includes a dichroic filter for separating emission bands and directing signals to different photomultiplier tubes (PMTs), which can be individually filtered to detect specific emission bands.

[0034] Figure 10B Provides Figure 10A a second view of the dual-channel, non-descanned detector design shown in DETAILED DESCRIPTION

[0035] Identifying tumor tissue during surgery is crucial for making appropriate surgical decisions and achieving the maximum safe resection for the patient. In recent years, intraoperative fluorescent contrast agents such as fluorescein, 5-aminolevulinic acid (5-ALA) (NX Development Corp, Lexington, KY), BLZ-100 (Blaze Bioscience, Inc., Seattle WA), and LUM015 (Lumicell, Inc., Newton, MA) have gained recognition and popularity. These contrast agents are typically administered preoperatively (e.g., orally or by injection) and can be observed with conventional fluorescence surgical microscopes (e.g., Zeiss OPMI Pentero 800 (Carl Zeiss Meditec, Inc., Dublin, CA), Leica PROvido-FL560 (Leica Microsystems Inc., Buffalo Grove, IL), or Synaptive Modus V (Synaptive Medical, Toronto, ON)). Figure 1A Examples of visible light images of brain tissue treated with 5-ALA during a surgical procedure for removing gliomas are provided. Figure 1B Corresponding fluorescent images are shown. In current practice, surgeons will resect all fluorescent tissue.

[0036] Limitations of this method well known in the art include: (i) limited sensitivity at the tumor margin where, due to low numbers of tumor cells, the concentration of contrast agent accumulation may be low, and (ii) limited specificity due to autofluorescence background signals, nonspecific staining, and non-uniform delivery.

[0037] To overcome these limitations, the methods and systems disclosed herein use high-resolution optical imaging in fresh tissue samples, in vitro or in vivo, to resolve dye distribution at the cellular or subcellular level in the operating room in near real-time. This method relies on optical sectioning to image fresh tissue samples. Suitable high-resolution optical imaging techniques that can be used to visualize fluorescent contrast agents in thick tissue samples without physically dissecting the tissue include, but are not limited to, two-photon fluorescence (2P) microscopy, confocal fluorescence microscopy (CM), light-sheet microscopy (LSM), and structured illumination microscopy (SIM). This imaging modality relies on optical sectioning to suppress out-of-focus signals and can have an axial resolution equal to or better than 10 microns and a lateral resolution equal to or better than 5 microns, i.e., sufficient resolution to image cellular and subcellular features. The in vitro method may be the preferred method for obtaining the best image quality and sensitivity. The in vivo method may be preferred from the perspective of the clinical workflow because they do not require tissue biopsy.

[0038] Definition: Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0039] As used in this specification and the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly indicates otherwise. Any reference to "or" herein is intended to cover "and / or" unless otherwise stated.

[0040] As used herein, the term "about" a number means that number plus or minus 10% of that number. When used in the context of a range, the term "about" means the range minus 10% of its minimum value and plus 10% of its maximum value.

[0041] Tissue sample: In some cases, the disclosed systems and methods can be used to characterize any of a variety of tissue samples known to those skilled in the art. Examples include, but are not limited to, connective tissue, epithelial tissue, muscle tissue, lung tissue, pancreatic tissue, breast tissue, kidney tissue, liver tissue, prostate tissue, thyroid tissue, and nerve tissue samples. In some cases, the tissue sample can be from any organ or component of a plant or animal. In some cases, the tissue sample can be from any organ or other component of the human body, including but not limited to the brain, heart, lungs, kidneys, liver, stomach, bladder, intestine, skeletal muscle, smooth muscle, breast, prostate, pancreas, thyroid, etc. In some cases, the tissue sample can include samples collected from patients exhibiting abnormalities or diseases (e.g., tumors or cancers (e.g., sarcoma, carcinoma, glioma, etc.)).

[0042] Tissue sample collection: The procedures for obtaining tissue samples from an individual are well known in the art. For example, procedures for extracting and processing tissue samples such as those from fine needle aspiration biopsy, core needle biopsy, or surgical biopsy are well known and can be used to obtain tissue samples for analysis using the disclosed systems and methods. Generally, to collect such tissue samples, a thin hollow needle is inserted into a mass such as a tumor mass, or the mass is surgically exposed to sample the tissue, and after staining, the tissue will be examined under a microscope.

[0043] Contrast agent: In some cases, the disclosed methods and systems include the use of a contrast agent to enhance and / or distinguish the appearance of tumor tissue, benign tissue, and malignant tissue in an image of a tissue sample. As used herein, the terms "contrast agent" and "optical contrast agent" may be used interchangeably. As used herein, the terms "fluorescent contrast agent" and "phosphorescent contrast agent" refer to subsets of optical contrast agents that emit fluorescence or phosphorescence, respectively, upon appropriate excitation. Examples of fluorescent contrast agents include, but are not limited to, 5-aminolevulinic acid hydrochloride (5-ALA), BLZ-100, and LUM015. In some cases, the disclosed methods and systems include the measurement of a signal obtained from a contrast agent (e.g., a cell-associated contrast agent), which may be a signal generated by the contrast agent itself or by a metabolite or processed form of the contrast agent.

[0044] 5-ALA is a compound that metabolizes intracellularly to form the fluorescent protoporphyrin IX (PPIX) molecule. Exogenous application of 5-ALA results in highly selective accumulation of PPIX in tumor cells and epithelial tissues. PPIX is excited by blue light (excitation maxima at approximately 405 nm and 442 nm) and emits red light (emission maxima at approximately 630 nm and 690 nm) (see, e.g., Wang et al. (2017), "Enhancement of 5-aminolevulinic acid-based fluorescence detection of side population-defined glioma stem cells by iron chelation", Nature Scientific Reports 7:42070).

[0045] BLZ-100 contains the tumor ligand chlorotoxin (CTX) conjugated to indocyanine green (ICG) and has shown potential as a targeted contrast agent for brain tumors and the like (see, e.g., Butte et al. (2014), "Near-infrared imaging of brain tumors using the Tumor Paint BLZ-100 to achieve near-complete resection of brain tumors", Neurosurg Focus 36(2):E1). BLZ-100 is typically excited in the near-infrared at approximately 780 nm (broad ICG absorption peak centered at approximately 800 nm) and has a broad fluorescence emission spectrum (emission maximum at approximately 810 nm - 830 nm) that almost overlaps the absorption spectrum.

[0046] LUM015 is a protease-activated imaging agent that comprises a commercially available fluorescent quencher molecule linked to a 20-kD polyethylene glycol (PEG) via a Gly-Gly-Lys-Arg (GGRK) peptide and a cyanine dye 5 (Cy5) fluorophore. The intact molecule is optically inactive, but upon proteolytic cleavage by cathepsin K, L, or S, etc., the quencher is released to generate an optically active fragment (see, e.g., Whitley et al. (2016) “A mouse-human phase 1 co-clinical trial of a protease-activated fluorescent probe for imaging cancer”, Science Translational Medicine 8(320) pp. 320ra4). The Cy5-labeled fragment has a fluorescence excitation peak at approximately 650 nm and an emission peak at 670 nm.

[0047] The fluorophore can be administered to a tissue sample using any of a variety of techniques known to those skilled in the art for in vitro imaging, e.g., by administering a contrast agent solution as a staining reagent to a tissue section. The contrast agent can be administered to a subject (e.g., a patient) using any of a variety of techniques known to those skilled in the art for in vivo imaging, including but not limited to oral, intravenous injection, etc., where the choice of technique may depend on the particular contrast agent.

[0048] For any of the imaging methods and systems disclosed herein, a tissue sample can in some cases be stained with one or more optical contrast agents. For example, in some cases, a tissue sample can be stained with one, two, three, four, or more than four different optical contrast agents.

[0049] As described above, in some cases, one or more optical contrast agents can include fluorescent contrast agents that emit a fluorescent signal. In some cases, one or more optical contrast agents can include phosphorescent contrast agents that emit a phosphorescent signal. In some cases, one or more optical contrast agents can include contrast agents that specifically bind to cells in a tissue sample. In some cases, one or more optical contrast agents can include contrast agents that specifically bind to one or more specific types of cells (e.g., “target” cells). In some cases, one or more optical contrast agents can comprise, for example, an antibody conjugated to a fluorophore, quantum dot, nanoparticle, or phosphor. In some cases, cell-associated contrast agents can comprise a fluorescent enzyme substrate designed to target cell types that express a specific enzyme.

[0050] Intra-operative use: In some cases, the disclosed methods and systems can be used intra-operatively, e.g., to guide a surgical procedure, identify locations for performing a biopsy, or determine whether an excision is complete. In some cases, the disclosed systems can be used in an operating room environment to provide real-time or near real-time in vivo imaging capabilities and guidance to a surgeon performing a surgical procedure.

[0051] High-resolution optical sectioning fluorescence and related imaging microscopy: Suitable high-resolution optical imaging techniques that can be used to visualize the distribution of fluorescent contrast agents in thick tissue samples without physically dissecting the tissue include, but are not limited to, two-photon fluorescence (2P or TPE) microscopy, three-photon fluorescence (3P) microscopy, confocal fluorescence microscopy (CFM), light-sheet microscopy (LSFM), and structured illumination microscopy (SIM). Generally, these techniques rely on, e.g., confocal optics and / or tight focusing of an excitation laser beam required to excite two-photon fluorescence and other optical processes for achieving a small depth of field capable of optically sectioning thick tissue samples. Some of these techniques are compatible with other light emission modes (e.g., phosphorescence and fluorescence).

[0052] Two-photon fluorescence microscopy: Two-photon (2P) fluorescence microscopy is a fluorescence imaging technique in which an electronic transition is excited by the absorption of two excitation photons by a dye molecule, resulting in the emission of a single emission photon that has a shorter wavelength than the wavelength of the excitation light. Two-photon fluorescence microscopy typically uses near-infrared (NIR) excitation light, which minimizes scattering in the tissue sample. The multi-photon absorption process also suppresses background signals (due to the non-linear interaction with the tissue sample, which confines the excitation of fluorescence mainly to the focal plane) and helps to increase the tissue penetration depth (the thickness can be up to about one millimeter). In addition to deeper tissue penetration, two-photon excitation also has the advantages of efficient light detection and reduced photobleaching (see, e.g., Denk et al., (1990), "Two-Photon Laser Scanning Fluorescence Microscopy", Science 248:4951:73–76).

[0053] Confocal fluorescence microscopy: Confocal fluorescence microscopy (CFM) is an imaging technique that provides three-dimensional optical resolution by actively suppressing laser-induced fluorescence signals from out-of-focus planes. This is typically achieved by using a pinhole in front of the detector such that light from the in-focus plane is imaged by the microscope objective and passes through the pinhole, while light from out-of-focus planes is largely blocked by the pinhole (see, e.g., Combs (2010) “Fluorescence Microscopy: A Concise Guide to Current Imaging Methods”, Curr. Protocols in Neurosci. 50(1):2.1.1–2.1.14; Sanai et al., (2011), “Intraoperative confocal microscopy in the visualization of 5-aminolevulinic acid fluorescence in low-grade gliomas”, J. Neurosurg. 115(4):740-748; Liu et al. (2014), “Trends in Fluorescence Image-guided Surgery for Gliomas”, Neurosurgery 75(1):61-71).

[0054] Light Sheet Fluorescence Microscopy: Light Sheet Fluorescence Microscopy (LSFM) uses a light plane (usually generated by expanding a laser beam to fill a cylindrical lens and / or a slit aperture) to optically section and observe tissues at subcellular resolution, and allows imaging deep within transparent tissues. Because the tissue is exposed to a thin sheet of light, photobleaching and phototoxicity are minimized compared to wide-field fluorescence microscopy, confocal microscopy, or multiphoton microscopy (see, e.g., Santi (2011), "Light Sheet Fluorescence Microscopy: A Review", J. of Histochem. & Cytochem. 59(2):129 - 138; Meza et al., (2015), "Comparing high-resolution microscopy techniques for potential intraoperative use in guiding low-grade glioma resections", Lasers in Surg. And Med. 47(4):289 - 295; Glaser et al., (2017), "Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens", Nat Biomed Eng. 1(7):0084).

[0055] Structured Illumination Microscopy: Structured Illumination Microscopy (SIM) is a method of obtaining optical sections in a conventional wide-field microscope by projecting a single spatial frequency grating pattern of the excitation light onto the sample. For example, images taken at three spatial positions of the grating are processed to produce an optical section image similar to that obtained using a confocal microscope (see, e.g., Neil et al. (1997), "Method of Obtaining Optical Sectioning By Using Structured Light In A Conventional Microscope", Optics Lett. 22(24):1905 - 1907).

[0056] High-Resolution Optical Sectioning Non-Fluorescent Imaging Microscopy: High-resolution optical sectioning non-fluorescent imaging microscopy applicable to tissue morphology imaging includes, but is not limited to, stimulated Raman scattering (SRS) microscopy, coherent anti-Stokes Raman scattering (CARS) microscopy, confocal reflection (CR) microscopy, second harmonic generation (SHG) microscopy, and third harmonic generation (THG) microscopy. Generally, these techniques rely on the tight focusing of an excitation laser beam required to excite SRS, CARS, SHG, or THG scattering or emission to achieve a small depth of field capable of optically sectioning and imaging thick tissue samples.

[0057] Stimulated Raman Scattering (SRS) Microscopy: Stimulated Raman scattering (SRS) microscopy is an imaging technique that provides rapid, label-free, high-resolution microscopic imaging of an untreated tissue sample. SRS microscopy requires two sequences of laser pulses that overlap in time such that the temporal mismatch is less than the pulse duration (e.g., less than 100 fsec), and the spatial overlap is less than the focal spot size (e.g., less than 100 nm). Stimulated Raman scattering imaging induced in the sample by this pair of spatially and temporally synchronized laser pulse sequences can map different molecular components within the tissue sample based on image acquisition corresponding to vibrational frequencies (or wave numbers) corresponding to, for example, the CH2-vibration (2,850 cm -1 ) of lipid molecules or the CH3-vibration (2,930 cm -1 ) of protein and nucleic acid molecules (see, for example, Freudiger et al., (2008), “Label-Free Biomedical Imaging with High Sensitivity by Stimulated Raman Scattering Microscopy”, Science 322: 1857-186; and Orringer et al., (2017), “Rapid Intraoperative Histology of Unprocessed Surgical Specimens via Fibre-Laser-Based Stimulated Raman Scattering Microscopy”, Nature Biomed. Eng. 1:0027).

[0058] Coherent anti-Stokes Raman scattering (CARS) microscopy: Coherent anti-Stokes Raman scattering (CARS) microscopy is a label-free imaging technique. CARS can form an image of the structure in a sample (e.g., a tissue sample) by showing characteristic intrinsic vibration contrasts caused by the molecular components of the sample (see, e.g., Camp et al., (2014), “High-speed coherent Raman fingerprint imaging of biological tissues”, Nat. Photon. 8, 627–634; and Evans et al., (2007), “Chemically-selective imaging of brain structures with CARS microscopy”, Opt. Express. 15, 12076–12087). This technique uses two high-power lasers to irradiate the sample, where the frequency of the first laser is typically kept constant, and the frequency of the second laser is adjusted so that the frequency difference between the two lasers is equal to the frequency of the Raman-active mode of interest. CARS is several orders of magnitude stronger than conventional Raman scattering.

[0059] Confocal reflectance (CR) microscopy: Confocal reflectance microscopy, which is performed using a confocal microscope to take advantage of the optical sectioning ability of confocal optics when operating in the reflection mode rather than the fluorescence mode, can be used to image unstained tissues or tissues labeled with a probe that reflects light. For example, near-infrared confocal laser scanning microscopy uses a relatively low-power laser beam tightly focused on a specific point in the tissue. Only the light backscattered from the focal plane is detected, and the contrast is caused by natural variations in the refractive index of the tissue microstructure. (See, e.g., Gonzalez et al., (2002), “Real-time, in vivo confocal reflectance microscopy of basal cell carcinoma”, J. Am. Acad. Dermatol. 47(6):869-874).

[0060] Second Harmonic Generation (SHG) Microscopy: SHG microscopy is a non-fluorescent multi-photon imaging technique. SHG utilizes second harmonic generation, a non-linear optical process where two excitation photons of a given wavelength interact with a material containing a non-centrosymmetric structure and are "converted" to form an emitted photon with half the wavelength of the excitation light. Exciting second harmonic light typically requires a laser source focused to a tight focal plane spot, and imaging the resulting light enables acquisition of high-resolution optical section images of biological tissues (including, for example, collagen fibers) (see, e.g., Bueno et al., (2016), "Second Harmonic Generation Microscopy: A Tool for Quantitative Analysis of Tissues", Chapter 5 Microscopy and Analysis , Stefan Stanciu, Ed., InTech Open).

[0061] Third Harmonic Generation (THG) Microscopy: THG microscopy is also a non-fluorescent multi-photon imaging technique. THG combines the advantages of label-free imaging and confinement of signal generation to the focus of the scanned laser. Third harmonic generation is a process where three excitation photons interact with matter to produce a single emitted photon with one-third the wavelength of the excitation light. It allows high-resolution optical section imaging of refractive index mismatches in biological tissues (see, e.g., Dietzel (2014), "Third harmonic generation microscopy", Wiley Analytical Science, Imaging and Microscopy, November 11, 2014).

[0062] Multispectral and / or multimodal imaging methods and systems for qualitative and quantitative detection of cell-related contrast agents: The disclosed imaging methods and systems include a novel combination of in vivo or in vitro multispectral and / or multimodal imaging to detect the distribution of optical contrast agents (e.g., cell-related fluorescent contrast agents) within tissue samples and to provide qualitative and / or quantitative measurements of the signals generated by the optical contrast agents (see, e.g., the review of conventional multimodal imaging techniques in Yue et al. (2011), "Multimodal nonlinear optical microscopy", Laser and Photonics Review 5(4):496-512). The disclosed multispectral and / or multimodal imaging methods and systems provide more accurate quantitative measurements of the signals generated by optical contrast agents by using image interpretation algorithms to correct background signals (e.g., autofluorescence background and / or highly fluorescent particles) and to resolve the measured signals at the cellular or subcellular level. The unexpected ability to resolve and measure signals generated by cell-related optical contrast agents at the cellular or subcellular level (partly due to improved detection sensitivity achieved through the use of a novel dual-wavelength fiber laser system as described in U.S. Patent No. 8,792,156; U.S. Patent No. 9,104,030; and U.S. Patent No. US 9,634,454) and improved image contrast) results in a significant quantitative improvement. In some cases, the disclosed multispectral and / or multimodal imaging methods and systems allow for the overlay of images, e.g., two-photon fluorescence images and stimulated Raman scattering images, to provide enhanced contrast and / or to generate pseudocolor images that aid in direct human interpretation.

[0063] In a first aspect, the disclosed imaging method (and a system configured to perform the method) may include using high-resolution optical sectioning microscopy techniques, including but not limited to two-photon microscopy, confocal fluorescence microscopy, light-sheet microscopy, or structured illumination microscopy, to acquire images at one, two, or more than two excitation wavelengths (or within one, two, or more than two excitation wavelength ranges) and at one, two, or more than two detection wavelengths (or within one, two, or more than two detection wavelength ranges). The disclosed imaging method (and a system configured to perform the method) then applies an image interpretation algorithm to detect contrast-positive cells and exclude background signals (e.g., highly fluorescent particles based on size, shape, pattern, or intensity) based on size, shape, pattern, or intensity to provide a quantitative measurement of the signal generated by a cell-associated contrast agent, wherein the quantitative measurement of the signal is resolved at the cellular level. Specific examples of this embodiment will be described in more detail below. In some cases, the image interpretation algorithm may provide qualitative and / or quantitative measurements from one or more signals associated with one or more optical contrast agents. For example, in some cases, the image interpretation algorithm may provide a "cellularity fraction", e.g., determining the number of cells (or cell nuclei). The results may also be presented as the density per unit area of the imaged tissue sample. In some cases, the image interpretation algorithm may provide the average signal from the cell-associated contrast agent. In some cases, the image interpretation algorithm may provide the average signal of contrast-positive cells after subtracting the background signal averaged over the entire image. In some cases, the image interpretation algorithm may provide the number of cells with a signal greater than a specific signal threshold that defines contrast-positive cells. In some cases, the image interpretation algorithm may provide the percentage of the total number of contrast-positive cells in the image. In some cases, the disclosed method and system may be used during surgery to identify locations for performing biopsies or to determine whether an excision is complete.

[0064] In a second aspect, the disclosed method (and a system configured to perform the method) combines cell imaging using one or more contrast agents of a first optical sectioning high-resolution microscopy (e.g., 2P, CFM, LSM, or SIM) (i.e., a first imaging mode) and a second optical sectioning high-resolution microscopy (e.g., SRS, CARS, CR, SHG, or THG) (i.e., a second imaging mode) for imaging tissue morphology in the same optical focal plane of a tissue sample, regardless of the presence of one or more contrast agents. The instrument then uses an image interpretation algorithm to process the images to provide the location and / or area of one or more individual cells based on one or more images acquired using the second imaging mode and to provide a quantitative measurement of the signal generated by one or more contrast agents in the tissue sample based on one or more images acquired using the first mode at one or more locations and / or areas corresponding to one or more cells, such that the quantitative measurement of the signal is resolved at the cellular level. Specific examples of this embodiment will be described in more detail below. In some cases, the instrument uses an image interpretation algorithm to process the images to provide the location and / or size of one or more individual cells based on one or more images acquired using the first imaging mode and to provide a quantitative measurement of the signal generated by one or more contrast agents in the tissue sample based on one or more images acquired using the first mode at one or more locations and / or areas corresponding to one or more cells. In some cases, the quantitative measurement can be obtained from the signal generated by one or more contrast agents at one or more locations and / or areas corresponding to one or more cells, or can be obtained from the signal (e.g., an SRS signal) of an image acquired using the second imaging mode at one or more locations and / or areas corresponding to one or more cells, or can be obtained from a combination of the signal generated by one or more contrast agents and the signal of an image acquired using the second imaging mode at one or more locations and / or areas corresponding to one or more cells. In some cases, the image interpretation algorithm can provide qualitative and / or quantitative measurements obtained from signals associated with one or more optical contrast agents. For example, in some cases, the image interpretation algorithm can provide: (i) the "cellularity fraction" as described above, (ii) the average signal obtained from cell-associated contrast agents, (iii) the average signal of contrast-positive cells after subtracting the background signal averaged over the entire image, (iv) the number of cells with a signal greater than a specific signal threshold defining contrast-positive cells, (v) the percentage of the total number of contrast-positive cells in the image, or any combination thereof. In some cases, the disclosed method and system can be used during surgery to identify locations for performing biopsies or to determine whether an excision is complete.

[0065] In a third aspect, the imaging system can be, for example, a multi-channel imaging microscope that simultaneously acquires a first image at the emission wavelength of the contrast agent and a second image outside the emission wavelength of the contrast agent, and then provides the multi-channel emission signal to an image interpretation algorithm that detects cells in the first image and suppresses non-specific background signals (e.g., autofluorescence background or highly fluorescent particles) based on measured spectral characteristics (e.g., by ratioing or thresholding the first image using signal data acquired in the second image, (e.g., at locations corresponding to one or more cells detected in the first image) or otherwise correcting the signal measured in the first image by a background value determined from the second image). In some cases, a multi-color image can be created based on applying a pseudo-color algorithm to the multi-channel image data (e.g., by designating the contrast agent-related signal as red and the background signal as green) to simplify the human interpretation of the image. The latter approach can obviate the need for a computer-aided interpretation algorithm. Examples of this embodiment of the disclosed imaging methods and systems will be described in more detail below.

[0066] Image acquisition parameters: For any of the imaging methods and systems disclosed herein, images of a tissue sample can be acquired using a variety of image acquisition parameter settings, including but not limited to effective image resolution, the number of excitation wavelengths used, the number of emission wavelengths at which the image is acquired, the number of images acquired within a particular time period, the number of different imaging modes for the images acquired, and the image acquisition parameter settings are subsequently processed and / or combined to: (i) create a multi-color or enhanced contrast image to facilitate, for example, the interpretation and identification of images of tumor tissue (if present in the tissue sample), and / or (ii) generate quantitative measurements based on one or more signals from one or more cell-related contrast agents and / or signals from a second imaging mode (e.g., a non-fluorescent imaging mode such as SRS).

[0067] In some cases, the disclosed imaging system can include a laser scanning system, e.g., a system that acquires images in two dimensions by scanning or rasterizing a laser spot on the optical focal plane of the imaging system, and the emitted or scattered light (e.g., two-photon fluorescence or stimulated Raman scattering light) is directed by the optical system to one or more photodetectors (e.g., photomultiplier tubes, avalanche photodiodes, solid-state near-infrared detectors, etc.). In some cases, if the imaging system includes two or more photodetectors, the two or more detectors can be of the same type or can be of different types. In some cases, the two or more different types of detectors differ in terms of size (diameter or cross-sectional area), integration time, signal-to-noise ratio, sensitivity, etc.

[0068] In some cases, the disclosed imaging system can include a laser scanning system that utilizes one or more image sensors or cameras. For example, in some cases, the disclosed imaging system can include one, two, three, four, or more than four image sensors or cameras. In some cases, for example, if the imaging system includes two or more image sensors or cameras, the image sensors or cameras can be the same or different in terms of pixel size, number of pixels, dark current, signal-to-noise ratio, detection sensitivity, etc. Thus, the images acquired by two or more image sensors or cameras can have different image resolutions. In some cases, one or more image sensors can have a number of pixels of about 0.5 megapixel, 1 megapixel, 2 megapixels, 4 megapixels, 6 megapixels, 8 megapixels, 10 megapixels, 20 megapixels, 50 megapixels, 80 megapixels, 100 megapixels, 200 megapixels, 500 megapixels, or 1000 megapixels (or any number of pixels within the range spanned by these values). In some cases, the pixel size within a given image sensor can be about 20μm, 10μm, 5μm, 3.5μm, 2μm, 1μm, 0.5μm, or 0.1μm (or any pixel size within the range spanned by these values). In some cases, one or more image sensors or cameras can be configured to bin separate pixel groups to change the effective resolution of the image thus acquired.

[0069] In some cases, the disclosed imaging system (scanning system or image sensor-based system) can be configured to acquire a single image for each of one or more specific excitation wavelengths, emission wavelengths, and / or imaging modes. In some cases, the disclosed system can be configured to acquire 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or more than 100 images (or any number of images within this range) for each of one or more specified excitation wavelengths, emission wavelengths, and / or imaging modes. In some cases, the disclosed system can be configured to acquire video data for each of one or more specified excitation wavelengths, emission wavelengths, and / or imaging modes.

[0070] In some cases, the disclosed imaging system can be configured to acquire one or more images for each of one or more specified excitation wavelengths, emission wavelengths, and / or imaging modes during a specified period of time. For example, in some cases, the disclosed system can be configured to acquire one or more images for each of one or more specified excitation wavelengths, emission wavelengths, and / or imaging modes every 0.1 millisecond, 1 millisecond, 10 milliseconds, 20 milliseconds, 30 milliseconds, 40 milliseconds, 50 milliseconds, 60 milliseconds, 70 milliseconds, 80 milliseconds, 90 milliseconds, 100 milliseconds, 200 milliseconds, 300 milliseconds, 400 milliseconds, 500 milliseconds, 600 milliseconds, 700 milliseconds, 800 milliseconds, 900 milliseconds, 1 second, 2 seconds, 3 seconds, 4 seconds, 5 seconds, 6 seconds, 7 seconds, 8 seconds, 9 seconds, 10 seconds, 20 seconds, 30 seconds, 40 seconds, 50 seconds, 1 minute, 2 minutes, 3 minutes, 4 minutes, 5 minutes, 6 minutes, 7 minutes, 8 minutes, 9 minutes, 10 minutes, 20 minutes, 30 minutes, 40 minutes, 50 minutes, 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours (or any period of time within this range). In some cases, the disclosed system can be configured to acquire video data for each of one or more specified excitation wavelengths, emission wavelengths, and / or imaging modes.

[0071] In some cases, the disclosed imaging system can be configured to acquire images using an exposure time (or integration time or image capture time) of approximately 1 millisecond, 5 milliseconds, 10 milliseconds, 25 milliseconds, 50 milliseconds, 75 milliseconds, 100 milliseconds, 250 milliseconds, 500 milliseconds, 750 milliseconds, 1 second, 2 seconds, 3 seconds, 4 seconds, 5 seconds, 6 seconds, 7 seconds, 8 seconds, 9 seconds, 10 seconds, 20 seconds, 30 seconds, 40 seconds, 50 seconds, 60 seconds or more than 60 seconds (or any exposure time, image capture time, or integration time within the range spanned by these values).

[0072] In some cases, including but not limited to cases utilizing two-photon fluorescence imaging, one, two, three, four or more than four different excitation wavelengths (or wavelength ranges) can be used to acquire an image. In some cases, excitation light of approximately 360 nm, 380 nm, 400 nm, 420 nm, 440 nm, 460 nm, 480 nm, 500 nm, 520 nm, 540 nm, 560 nm, 580 nm, 600 nm, 620 nm, 640 nm, 660 nm, 680 nm, 700 nm, 720 nm, 740 nm, 760 nm, 780 nm, 800 nm, 820 nm, 840 nm, 860 nm, 880 nm, 900 nm, 920 nm, 940 nm, 960 nm, 980 nm, 1000 nm, 1020 nm, 1040 nm, 1060 nm, 1080 nm, 1100 nm, 1120 nm, 1140 nm, 1160 nm, 1180 nm or 1200 nm can be used to acquire an image, wherein the one or more excitation wavelengths used will typically be selected based on the choice of the optical contrast agent used to stain the tissue sample. In some cases, light of one, two, three, four or more than four excitation wavelengths can be provided by one, two, three, four or more than four lasers or other light sources. In some cases, an optical glass filter, bandpass filter, interference filter, longpass filter, shortpass filter, dichroic reflector, monochromator or any combination thereof can be used to select the excitation wavelength (or wavelength range).

[0073] In some cases, including but not limited to cases utilizing two-photon fluorescence imaging, images of one, two, three, four, or more than four different emission (or detection) wavelengths (or emission (or detection) wavelength ranges) can be acquired. In some cases, images of light emitted at about 360 nm, 380 nm, 400 nm, 420 nm, 440 nm, 460 nm, 480 nm, 500 nm, 520 nm, 540 nm, 560 nm, 580 nm, 600 nm, 620 nm, 640 nm, 660 nm, 680 nm, 700 nm, 720 nm, 740 nm, 760 nm, 780 nm, 800 nm, 820 nm, 840 nm, 860 nm, 880 nm, 900 nm, 920 nm, 940 nm, 960 nm, 980 nm, 1000 nm, 1020 nm, 1040 nm, 1060 nm, 1080 nm, 1100 nm, 1120 nm, 1140 nm, 1160 nm, 1180 nm, or 1200 nm can be acquired, where the one or more detection (emission) wavelengths used are typically selected based on the choice of optical contrast agent used to stain the tissue sample. In some cases, an optical glass filter, bandpass filter, interference filter, longpass filter, shortpass filter, dichroic reflector, monochromator, or any combination thereof can be used to select the emission (or detection) wavelength (or emission (or detection) wavelength range).

[0074] In some cases, excitation and / or emission (detection) wavelength ranges can be used to acquire images, where the wavelength range includes a bandwidth of about 10 nm, 20 nm, 30 nm, 40 nm, 50 nm, 60 nm, 70 nm, 80 nm, 90 nm, 100 nm, or more than 100 nm. In some cases, an optical glass filter, bandpass filter, interference filter, longpass filter, shortpass filter, dichroic reflector, monochromator, or any combination thereof can be used to select the bandpass for the excitation and / or emission wavelength range.

[0075] In some cases, the lateral resolution of the images acquired by the disclosed imaging system can be less than 20 μm, 15 μm, 10 μm, 9 μm, 8 μm, 7 μm, 6 μm, 5 μm, 4 μm, 3 μm, 2 μm, 1 μm, 0.5 μm, or 0.25 μm. In some cases, the lateral resolution of the disclosed imaging system may be limited by the size of the focused laser spot.

[0076] In some cases, the axial resolution of an image acquired by the disclosed imaging system can be less than 50 μm, 20 μm, 15 μm, 10 μm, 9 μm, 8 μm, 7 μm, 6 μm, 5 μm, 4 μm, 3 μm, 3 μm, 1 μm, or 0.5 μm. In some cases, the axial resolution of the disclosed imaging system may be limited by the size of the focused laser spot. In some cases, the axial resolution of the disclosed imaging system may be limited by the diameter of the pinhole aperture in the confocal optical system.

[0077] In some cases, the disclosed imaging system is configured to acquire images of a tissue sample at the same focal plane for two or more imaging modes. In some cases, if the focal planes of two different imaging modes are offset from each other by less than 10 μm, less than 9 μm, less than 8 μm, less than 7 μm, less than 6 μm, less than 5 μm, less than 4 μm, less than 3 μm, less than 2 μm, less than 1 μm, less than 0.5 μm, or less than 0.25 μm, the focal planes of the two different imaging modes can be considered to be "the same" (or coplanar).

[0078] In some cases, including but not limited to those cases using SRS imaging, images can be acquired at one or more selected wavenumbers (or wavenumber spectral ranges) corresponding to the Raman shifts of different chemical groups. In some cases, images can be acquired at one, two, three, four, or more than four different wavenumbers (or wavenumber spectral ranges). Examples of the wavenumber ranges corresponding to the Raman shifts of specific chemical groups include but are not limited to those listed in Table 1.

[0079] Table 1 Examples of Raman shift data for different chemical groups

[0080]

[0081] In some cases, including but not limited to those cases using SRS imaging, images can be acquired over a spectral range spanning from about 100 cm -1 to about 3000 cm -1 . In some cases, images can be acquired over a spectral range spanning at least 100 cm -1 , at least 125 cm -1 , at least 150 cm -1 , at least 200 cm -1 , at least 250 cm -1 , at least 300 cm -1 , at least 350 cm -1 , at least 400 cm -1 , at least 450 cm -1 , at least 500 cm -1 , at least 550 cm -1 , at least 600 cm-1 、 at least 650 cm -1 、 at least 700 cm -1 、 at least 750 cm -1 、 at least 800 cm -1 、 at least 900 cm -1 、 at least 1000 cm -1 、 at least 1100 cm -1 、 at least 1200 cm -1 、 at least 1300 cm -1 、 at least 1400 cm -1 、 at least 1500 cm -1 、 at least 1750 cm -1 、 at least 2000 cm -1 、 up to 2250 cm -1 、 at least 2500 cm -1 、 at least 2750 cm -1 or at least 3000 cm -1 to obtain an image in the spectral range of up to 3000 cm -1 、 up to 2750 cm -1 、 up to 2500 cm -1 、 up to 2250 cm -1 、 up to 2000 cm -1 、 up to 1750 cm -1 、 up to 1500 cm -1 、 up to 1400 cm -1 、 up to 1300 cm -1 、 up to 1200 cm -1 、 up to 1100 cm -1 、 up to 1000 cm -1 、 up to 900 cm -1 、 up to 800 cm -1 、 up to 750 cm -1 、 up to 700 cm -1 、 up to 650 cm -1 、 up to 600 cm -1 、 up to 550 cm -1 、 up to 500 cm -1 、 up to 450 cm -1 、 up to 400 cm -1 、 up to 350 cm -1 、 up to 300 cm -1 、 up to 250 cm -1 、 up to 200 cm -1 or up to 150 cm -1acquire an image within the spectral range of. In some cases, it is possible to use a spectral range spanning, for example, approximately 250 cm -1 to acquire an image. Those skilled in the art will understand that an image can be acquired within a spectral range at any position within any range defined by these values (e.g., from a range of approximately 200 cm -1 to a range of approximately 760 cm -1 ).

[0082] Multispectral and / or multimodal imaging system components: For any of the embodiments described herein, the disclosed imaging system can include one or more excitation light sources (e.g., solid-state lasers, fiber lasers, etc.), one or more image sensors or photodetectors (e.g., photomultiplier tubes, avalanche photodiodes, solid-state near-infrared detectors, charge-coupled device (CCD) sensors or cameras, CMOS image sensors or cameras, etc.), one or more scanning mirrors or translation stages, and additional optical components (e.g., objective lenses, additional lenses, mirrors, prisms, filters, colored glass filters, narrowband interference filters, broadband interference filters, dichroic mirrors, diffraction gratings, monochromators, apertures, optical fibers, optical waveguides, etc. or any combination thereof). In some cases, the disclosed imaging system can include one, two, three, four, or more than four lasers that provide excitation light at one, two, three, four, or more than four excitation wavelengths. In some cases, excitation light at one, two, three, four, or more than four excitation wavelengths can be transmitted to the optical focal plane through an objective lens for imaging a tissue sample (e.g., by using an appropriate combination of mirrors, dichroic reflectors, or beam splitters). In some cases, excitation light at one, two, three, four, or more than four excitation wavelengths can be transmitted to the optical focal plane using an optical path that does not include an objective lens for imaging the sample. In some cases, the disclosed imaging system is configured to acquire images of two or more imaging modes in the same optical plane (or focal plane) within a tissue sample. In some cases, the disclosed imaging system is configured to acquire images of two or more imaging modes in the same field of view within a tissue sample. In some cases, the disclosed imaging system can include one or more processors or computers, as will be discussed in more detail below. In some cases, an instrument designed to acquire images using a first imaging mode (e.g., SRS imaging or 2P imaging) can be modified to acquire images using a second imaging mode (e.g., 2P imaging or SRS imaging, respectively) simultaneously or sequentially.

[0083] Non-limiting examples of stimulated Raman scattering (SRS) imaging systems have been described in Orringer et al. (2017), “Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy”, Nature Biomed. Eng. 1:0027. A fully integrated SRS imaging system comprises five main components: (1) a fibre-coupled microscope with a motorized stage; (2) a dual-wavelength fibre laser module; (3) a laser control module; (4) a microscope control module; and (5) a computer for image acquisition, display and processing. The dual-wavelength fibre laser is designed taking advantage of the fact that the different frequencies of two main fibre gain media, erbium and ytterbium, overlap with the high wavenumber region of the Raman spectrum. The two synchronized narrowband laser pulse trains required for SRS imaging are generated by narrowband filtering of a broadband supercontinuum obtained from a single fibre oscillator and subsequent amplification in their respective gain media. The development of an all-fibre system based on polarization-maintaining components has significantly improved the laser stability of previous non-polarization-maintaining implementations. To be able to achieve high-speed diagnostic-quality imaging (e.g., acquire a 1 megapixel image in about 2 seconds per wavelength) with a signal-to-noise ratio comparable to that achievable with solid-state lasers, the laser output power is scaled to about 120 mW for a fixed-wavelength 790 nm pump beam and to about 150 mW for a tunable Stokes beam over the entire tuning range from 1,010 nm to 1,040 nm at a laser pulse repetition rate of 40 MHz and a transform-limited laser pulse duration of 2 picoseconds. Custom laser controller electronics are developed to tightly control the operating settings of the laser system using a microcontroller. A noise cancellation scheme based on auto-balanced detection is used to further improve the image quality, in which a portion of the laser beam is sampled to provide a measurement of the laser noise, which can be subtracted in real time. In some cases, a system such as the described SRS imaging system can be modified to simultaneously or sequentially acquire, for example, two-photon fluorescence images by providing at least one additional excitation laser at an appropriate wavelength and at least one additional image sensor or camera, where the at least one additional excitation beam and the emitted two-photon fluorescence are coupled to the SRS imaging system using a combination of dichroic reflectors, beam splitters, etc.

[0084] Image processing and image interpretation: In some cases, the disclosed imaging methods may include using image processing and / or image interpretation algorithms for processing the acquired images and providing qualitative and / or quantitative measurements of signals obtained from one or more cell-associated contrast agents and / or signals obtained from non-fluorescent imaging modalities. In some cases, the image processing and / or image interpretation algorithms may process images acquired using a first imaging modality (e.g., a first optical sectioning imaging modality) to identify cells and determine their locations. In some cases, the image processing and / or image interpretation algorithms may process images acquired using a second imaging modality different from the first imaging modality (e.g., a second optical sectioning imaging modality) to identify cells and determine their locations. In some cases, the image processing and / or image interpretation algorithms may process images acquired using one or both of the first imaging modality and the second imaging modality to identify cells and determine their locations.

[0085] In some cases, the image interpretation algorithms may include any one of a variety of conventional image processing algorithms known to those skilled in the art. Examples include, but are not limited to, the Canny edge detection method, the Canny-Deriche edge detection method, the first-order gradient edge detection method (e.g., the Sobel operator), the second-order differential edge detection method, the phase consistency (phase coherence) edge detection method, other image segmentation algorithms (e.g., intensity thresholding, intensity clustering methods, intensity histogram-based methods, etc.), feature and pattern recognition algorithms (e.g., the generalized Hough transform for detecting arbitrary shapes, the circular Hough transform, etc.), and mathematical analysis algorithms (e.g., Fourier transform, fast Fourier transform, wavelet analysis, autocorrelation, etc.) or any combination thereof. In some cases, such image processing algorithms may be used to detect individual cells within an image based on, for example, feature size, shape, pattern, intensity, or any combination thereof.

[0086] In some cases, the image interpretation algorithm can include trained artificial intelligence or machine learning algorithms, for example, to further improve the capabilities of the image processing and / or image interpretation algorithm to identify individual cells in an image and / or distinguish normal and abnormal tissue (e.g., tumor tissue) based on qualitative and / or quantitative measurements, where the qualitative and / or quantitative measurements are sourced from signals obtained from one or more contrast agents and / or signals obtained from non-fluorescent imaging modalities. Any one of a variety of machine learning algorithms can be used to implement the disclosed methods and systems. Examples include but are not limited to supervised learning algorithms, unsupervised learning algorithms, semi-supervised learning algorithms, deep learning algorithms, or any combination thereof. In some cases, the machine learning algorithm can include an artificial neural network algorithm, a deep convolutional neural network algorithm, a deep recurrent neural network, a generative adversarial network, a support vector machine, a hierarchical clustering algorithm, a Gaussian process regression algorithm, a decision tree algorithm, a logical model tree algorithm, a random forest algorithm, a fuzzy classifier algorithm, a k-means algorithm, an expectation maximization algorithm, a fuzzy clustering algorithm, or any combination thereof.

[0087] As a non-limiting example, in some cases, the machine learning algorithm for further improving the capabilities of the image processing and / or image interpretation algorithm to identify individual cells within an image and / or distinguish normal tissue from abnormal tissue (e.g., tumor tissue) can include an artificial neural network (ANN), for example, a deep learning algorithm. An artificial neural network typically includes a set of interconnected "nodes" (or "neurons") that form multiple layers. A typical ANN architecture can include an input layer, at least one or more hidden layers, and an output layer. An ANN can include any total number of layers and any number of hidden layers, where the hidden layers serve as training feature extractors that map an input data set to an output value or set of output values. As described above, each layer of the neural network includes a plurality of nodes. A node receives an input that is either directly from the input data (e.g., raw image data and / or preprocessed image data) or the output of the nodes in the previous layer, and the node performs a specific operation, such as a summation operation. In some cases, the connections from the input to the node are associated with weights (or weight factors). In some cases, for example, a node might multiply all pairs of inputs x i and their associated weights w iThey are added. In some cases, the weighted sum is offset by the bias b. In some cases, the output of the neuron can be gated using a threshold or an activation function f, which may be a linear or non-linear function. The activation function can be, for example, a rectified linear unit (ReLU) activation function or other functions, such as a saturated hyperbolic tangent function, an identity, a binary step function, a logistic function, an arctangent function, a softsign function, a parametric rectified linear unit, an exponential linear unit, a softPlus function, a bent identity function, a softExponential function, a sinusoid function, a sine function, a Gaussian function, or a sigmoid function, or any combination thereof.

[0088] One or more training data sets can be used to "teach" or "learn" the weight factors, bias values, and thresholds or other computational parameters of the neural network during the training phase. For example, input data from a training data set (e.g., raw image data and / or preprocessed image data) and gradient descent or backpropagation methods can be used to train the parameters such that one or more output values computed by the ANN (e.g., classification of a given tissue sample including tumor tissue) are consistent with the instances contained in the training data set.

[0089] In some cases, machine learning algorithms can be trained using one or more training data sets that include, for example, imaging data obtained from archived histopathological tissue samples (e.g., formalin-fixed tissue samples or fresh frozen tissue samples), imaging data obtained from fresh histopathological tissue samples, or any combination thereof. In some cases, imaging data obtained by two or more systems deployed at the same or different sites can be used to continuously, periodically, or randomly update the training data set. In some cases, two or more systems are deployed at different sites, and the training data set is optionally stored in a cloud-based database and / or updated continuously, periodically, or randomly via a network connection.

[0090] As described above, in some cases, an image interpretation algorithm provides a "cellularity score", e.g., determining the number of cells (or cell nuclei) identified per unit area of an imaging sample. In some cases, an image interpretation algorithm provides an average signal obtained from a cell-associated contrast agent. In some cases, an image interpretation algorithm provides an average signal of contrast-positive cells after subtracting a background signal averaged over the entire image. In some cases, an image interpretation algorithm provides the number of cells with a signal greater than a specific signal threshold that defines contrast-positive cells. In some cases, an image interpretation algorithm provides a percentage of the total number of contrast-positive cells in an image. As described above, in some cases, an image interpretation algorithm can be configured to generate a quantitative measurement based on one or more signals obtained from one or more cell-associated contrast agents and / or signals obtained from a second imaging modality (e.g., a non-fluorescent imaging modality such as SRS or one of the other non-fluorescent imaging modalities described herein).

[0091] In some cases, the imaging methods and systems of the present disclosure can include using a pseudocolor algorithm to transform an image acquired using any one of one or more imaging modalities to generate a multicolored image that provides, e.g., enhanced contrast of tissue structure, provides, e.g., enhanced detection of tumor tissue, and / or facilitates human interpretation of the image. In some cases, using a pseudocolor algorithm to transform an image acquired using any one of one or more imaging modalities can facilitate human interpretation of one or more images without implementing an additional image interpretation algorithm. In some cases, pseudocolored images generated from images acquired using any one of one or more imaging modalities can then be combined (e.g., subjected to linear or non-linear algebraic operations) to provide, e.g., enhanced contrast of tissue structure, provide, e.g., enhanced detection of tumor tissue, and / or facilitate human interpretation of the image.

[0092] Software and Computer-Readable Media: Aspects of the disclosed algorithms (or computer-implemented methods) may be considered a “product” or “article of manufacture,” e.g., a “computer program or software product,” typically in the form of processor-executable code and / or associated data stored in a type of computer-readable medium, where the processor-executable code includes a plurality of instructions for controlling a computer or computer system when executing one or more of the methods disclosed herein. Accordingly, disclosed herein is a computer-readable medium (“computer program or software product”) that includes an encoded instruction set (i.e., software) that, when executed by a processor, directs the processor to perform a series of logical steps to execute any of the methods disclosed herein. For example, disclosed herein is a computer-readable medium (“computer program or software product”) that includes an encoded instruction set (i.e., software) that, when executed by a processor, directs the processor to perform a series of logical steps to: (i) acquire an image using one or more of the imaging modalities disclosed herein, (ii) perform manual, semi-automatic, or automatic processing on the acquired image to identify one or more individual cells in the image, (iii) perform further manual, semi-automatic, or automatic processing on the acquired image to extract a quantitative measurement of a signal derived from a cell-related contrast agent at one or more individual cell locations, (iv) perform further manual, semi-automatic, or automatic processing on the acquired image to extract a quantitative measurement of a signal derived from a non-fluorescent image of the same tissue sample at one or more locations of one or more individual cells, or (v) any combination thereof.

[0093] The processor-executable (or machine-executable) code may be stored in an optical storage unit, which includes, for example, an optically-readable medium such as a compact disc, CD-ROM, DVD, or Blu-ray disc. The processor-executable code may be stored in an electronic storage unit or on a hard disk, which is, for example, a memory such as a read-only memory, random access memory, flash memory. “Storage” type media includes any and all tangible memory of a computer, computer system, etc. or its associated modules, such as various semiconductor memory chips, optical drives, tape drives, disk drives, etc., which may provide non-transitory storage for software encoding the methods and algorithms disclosed herein at any time.

[0094] All or part of the software code can sometimes communicate via the Internet or various other telecommunications networks. For example, such communication can load software from one computer or processor to another, such as from a management server or host to the computer platform of an application server. Thus, other types of media that can be used to convey software coding instructions include light waves, radio waves, and electromagnetic waves, such as those used across physical interfaces between local devices, over wired and optical landline networks, and over various atmospheric telecommunications links. Physical elements that carry such waves (such as wired or wireless links, optical links, etc.) are also considered media for conveying software coding instructions for performing the methods disclosed herein.

[0095] Computer processors: In some cases, the disclosed imaging systems can include one or more processors or computers that are individually or jointly programmed to provide instrument control and / or image processing functionality in accordance with the methods disclosed herein. One or more processors can include hardware processors such as a central processing unit (CPU), a graphics processing unit (GPU), a general processing unit, or a computing platform. One or more processors can include any one of a variety of suitable integrated circuits (e.g., application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs)), microprocessors, emerging next-generation microprocessor designs (e.g., memristor-based processors), logic devices, etc. Although the present disclosure is described with reference to processors, other types of integrated circuits and logic devices can also be applied. The processor can have any suitable data processing capabilities. For example, the processor can perform 512-bit, 256-bit, 128-bit, 64-bit, 32-bit, or 16-bit data operations. One or more processors can be single-core or multi-core processors, or multiple processors configured for parallel processing.

[0096] In some cases, one or more processors or computers for implementing the disclosed imaging methods can be part of a larger computer system and / or can be operatively coupled to a computer network (“network”) with the help of a communication interface to facilitate data transmission and sharing. The network can be a local area network, an intranet and / or an extranet, an intranet and / or an extranet that communicates with the Internet, or the Internet. The network is in some cases a telecommunications and / or data network. The network can include one or more computer servers that are in some cases capable of distributed computing, such as cloud computing. In some cases, with the help of a computer system, the network can implement a peer-to-peer network, which enables devices coupled to the computer system to act as clients or servers.

[0097] In some cases, the disclosed imaging system may further include a memory or memory location (e.g., random access memory, read-only memory, flash memory, etc.), an electronic storage unit (e.g., a hard disk), and a communication interface (e.g., a network adapter) for communicating with one or more other imaging systems and / or peripheral devices such as data storage and / or electronic display adapters.

[0098] In some cases, the data storage unit stores files such as drivers, libraries, and saved programs. The storage unit may also store user data, such as user-specific preferences, user-specific programs, and image data obtained during testing or using the disclosed imaging system. In some cases, a computer system or network may include one or more additional data storage units located external to the computer system, such as a data storage unit located on a remote server that communicates with the computer system via an intranet or the Internet.

[0099] Embodiments

[0100] These embodiments are provided for illustrative purposes only and do not limit the scope of the claims provided herein.

[0101] Embodiment 1 - Two-Photon Fluorescence Imaging of Brain Tissue Administered with 5-ALA

[0102] We used Figure 2 the 2P imaging mode of the combined 2P / SRS microscope shown, with two excitation beams having wavelengths of 790 nm and 1020 nm, respectively, and a 640 nm / 80 nm bandpass detection filter to image in vitro samples of brain tumor tissue administered with 5-ALA as a contrast agent ( Figure 3A ). Figure 3B Shown is Figure 3A the same two-photon fluorescence image of the brain tumor tissue sample shown in

[0103] after processing the image to identify individual cells (circles) in the image. To our knowledge, this is the first time to observe individual cancer cells based on 5-ALA in tissue, and it constitutes a breakthrough in detection sensitivity. We were able to resolve individual cells and found that the contrast agent accumulates in the cytoplasm and does not penetrate the cell nucleus. Figure 3A ). Surprisingly, these are still visible in control samples from tissue not administered with 5-ALA, and thus this is attributed to the autofluorescence background.

[0104] When the evaluation was extended to in vivo imaging of human tissue samples using our two-photon microscope (e.g., Figure 4A; brain tissue from patients administered 5-ALA), we experienced a similar unexpected nonspecific contrast mechanism, where the 640 nm fluorescence signal was also detected in tissue samples from patients not administered 5-ALA contrast agent ( Figure 4B ). Specifically, in Figure 4B It can be seen that Figure 4A The cell-associated fluorescent signal and general "cloudiness" visible in the MRI were not visible in patients not given contrast agent, but the fluorescent particles were visible in both cases.

[0105] This was surprising because such nonspecificity had not been disclosed in previous work using conventional (single-photon fluorescence) surgical microscopes to image contrast agents. In fact, the US FDA has approved this contrast agent for clinical use based on data showing excellent sensitivity. We hypothesized that this nonspecificity might be due to the known less specific nature of two-photon excitation. We investigated the two-photon emission spectra of brain tumor tissue samples dosed with 5-ALA and found that the characteristic spectral peak of 5-ALA (between 620 nm and 650 nm) is indeed present on top of a large and spectrally broad background signal ( Figure 5 )superior.

[0106] Therefore, we began imaging the samples using single-photon confocal fluorescence microscopy. Figure 6 , right), i.e., by imaging the sample without significant optical sectioning, we regain the excellent specificity of the contrast agent, where the emission peak is visible without a large and spectrally broad background ( Figure 6 , left). However, when we closed the pinhole to produce the optical sectioning required for imaging single cells within thick, unsectioned tissue samples (i.e., reducing the depth of field), this spectral specificity was lost and only spectrally broad particles were visible, as observed in two-photon images. We hypothesize that this may be due to the enhanced photobleaching found in single-photon excitation at the focal plane, where the photon flux is highest, and the contrast agent must bleach more rapidly compared to the background signal. Therefore, in contrast to macroscopic imaging (e.g., using a conventional surgical microscope), cellular imaging using contrast agents such as 5-ALA using 2P imaging faces the dilemma that 2P imaging can provide sensitivity for imaging single contrast-positive cells but is inherently less specific, while 1P imaging suffers from photobleaching at the focal plane and therefore suffers from limited sensitivity or reduced imaging speed. In either case, measurement of the mean fluorescence intensity of the image will not provide an accurate readout of the tumor burden in the image.

[0107] Example 2 - Dual-mode imaging for enhanced sensitivity and specificity

[0108] This embodiment combines SRS microscopy and 2P microscopy to image tissue morphology and image the distribution of a fluorescent contrast agent ( Figure 2 ). A dual-wavelength fiber laser system (light generated at, for example, 1020 nm and 790 nm) is used to excite the sample and a photodetector (PD) is used to detect the SRS signal in transmission. After blocking the excitation light with a filter, we also use a second photodetector (e.g., a photomultiplier tube (PMT)) to detect the 2P signal in reflection. Images are acquired by raster-scanning the laser focus point-by-point through the sample with a computer-controlled galvanometric scanning mirror. In this multimodal microscope, SRS and 2P images can be acquired simultaneously.

[0109] The energy levels of these two techniques are shown in Figure 7 . In SRS, if the energy difference matches the energy difference of molecular vibrations, the molecule is excited from the ground state to the vibrational state based on the stimulated excitation of pump photons ( Figure 7 , left, upward arrow) and Stokes photons ( Figure 7 , left, downward arrow). In 2P, the electronic state of the contrast agent is excited by simultaneously absorbing two photons (e.g., two pump photons, or two Stokes photons, or one pump photon and one Stokes photon) ( Figure 7 , right, upward arrow), and the molecule then relaxes back to the ground state while emitting fluorescent photons ( Figure 7 , right, downward arrow) that can be detected.

[0110] Figure 8A The image in shows the SRS image of a brain cancer tissue sample at the CH2 - vibrational frequency of lipids (e.g., 2850 cm -1 ). At this wavenumber, the image has positive signals in the cytoplasm and intercellular space, and the nucleus is dark (due to the lack of lipids). This contrast can be used to definitively identify cells based on the identification of the nucleus using information including, for example, the size, shape, pattern, and / or signal intensity of features in the image. Advantageously, imaging the same tissue at the CH3 - vibrational frequency of proteins and nucleic acids (e.g., at 2930 cm -1 ) provides additional contrast for the cell nucleus. The SRS imaging channel is independent of the contrast agent (2P fluorescence) imaging channel. Other label-free imaging methods, such as CARS, CR, or THG, can also be used as alternative imaging modalities to SRS.

[0111] Figure 8B The image in shows the 2P fluorescence microscopy image of the 5-ALA contrast agent distribution acquired at the same location as described above in Figure 8A . Comparing the two images shows that some but not all cells identified based on the SRS image also have positive cell signals in the fluorescence channel.

[0112] The imaging system can use automatic image processing to determine the location and / or size of nuclei or cells from SRS images ( Figure 9A , circles) to measure the fluorescence intensity within the area of an individual cell ( Figure 9B , circles) to provide a measurement of the fluorescence signal generated by a cell-associated contrast agent. It can also determine whether the fluorescence signal within a selected area is above a specific threshold level and provide a binary output as to whether a particular cell is positive or negative for the contrast agent. This allows for a quantitative measurement of the percentage of contrast-positive cells in the image.

[0113] In some cases, the disclosed imaging system can provide high-resolution optical section Raman images at wavenumbers of 2,850 cm -1 and 2,930 cm -1 . In addition to these images, the disclosed imaging system can provide high-resolution optical section images of one or more fluorescence channels (emission wavelength ranges). Using Raman data, the image interpretation algorithm is able to spatially identify cell features and determine the cellularity fraction. Combining these metrics and data with the fluorescence channels that are inputs to the image interpretation algorithm creates a set of metrics that correlates the fluorescence data with the Raman data. Examples of these metrics include, but are not limited to, the number of cells identified, the number of cells associated with positive fluorescence, the percentage of cells associated with positive fluorescence, the ratio of positive-fluorescence cells to negative-fluorescence cells, and the ratio of positive-fluorescence cells to total cells, or any combination thereof.

[0114] Example 3 - Dual-Wavelength Fluorescence Imaging

[0115] In this example, the imaging system is a multi-channel 2P microscope that simultaneously acquires a first fluorescence image at the emission wavelength of the contrast agent (e.g., for 5-ALA, at 640 nm ± 40 nm) and a second fluorescence image outside the contrast agent emission wavelength (e.g., for 5-ALA, at <600 nm) as a measurement of non-specific background. The multi-channel emission signal can then be provided to an image processing or image interpretation algorithm, and the non-specific background signal can be suppressed based on the measured spectral characteristics of the contrast agent specificity and the background signal (e.g., by ratio or threshold). As described above, a multi-color image can be created based on applying a pseudocolor algorithm to the multi-channel image data to facilitate human interpretation of the image in real-time or near real-time.

[0116] Figure 10A and Figure 10BAn example of a dual-channel non-scanning (external) detector design for collecting fluorescence signals in two different emission wavelength ranges is shown, where dichroic filters are used to separate the emission bands and direct the signals to two photomultiplier tubes (PMTs), which are individually filtered to detect their respective emission bands. This design can be extended to include additional detectors (>2) to further improve sensitivity and specificity.

[0117] While the preferred embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that these embodiments are provided by way of example only. Many variations, changes, and substitutions will occur to those skilled in the art without departing from the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in any combination when practicing the present invention. The following claims are intended to define the scope of the present invention and thereby cover methods and structures and equivalents thereof falling within the scope of these claims.

Claims

1. A system for imaging a tissue sample, comprising: a) A first optical subsystem configured to acquire a high-resolution image of the distribution of a contrast agent within the tissue sample using a first optical sectioning imaging mode; b) A second optical subsystem configured to acquire a high-resolution image of the morphology of the tissue sample using a second optical sectioning imaging mode, wherein the first optical subsystem and the second optical subsystem are configured to image the same optical plane within the tissue sample; and c) A processor configured to (i) process the high-resolution image of the distribution of the contrast agent and / or the high-resolution image of the morphology of the tissue sample to identify individual cells and determine the positions of the individual cells, (ii) process the high-resolution image of the distribution of the contrast agent to obtain the signal of the contrast agent at the positions of the individual cells, (iii) measure a background value at the positions of the individual cells from the high-resolution image of the distribution of the contrast agent; (iv) correct the signal of the contrast agent at the positions of the individual cells using the background value; and (v) output a quantitative measurement of the signal of the contrast agent.

2. The system according to claim 1, wherein the first optical sectioning imaging mode comprises two-photon fluorescence microscopy, confocal fluorescence microscopy, light-sheet microscopy, or structured illumination microscopy.

3. The system according to claim 1, wherein the second optical sectioning imaging mode comprises stimulated Raman scattering microscopy, coherent anti-Stokes Raman scattering microscopy, confocal reflection microscopy, second harmonic generation microscopy, or third harmonic generation microscopy.

4. The system according to claim 1, wherein the first optical subsystem and the second optical subsystem are configured to have an axial resolution of less than 10 μm.

5. The system according to claim 1, wherein the first optical subsystem and the second optical subsystem are configured to have a lateral resolution of less than 5 μm.

6. The system according to claim 1, wherein the first optical subsystem is further configured to acquire high-resolution images in two or more detection wavelength ranges.

7. The system according to claim 6, wherein: The two or more detection wavelength ranges include a first detection wavelength range and a second detection wavelength range, wherein the first detection wavelength range includes an emission peak of the contrast agent, and the second detection wavelength range does not include the emission peak of the contrast agent; and wherein the processor is further configured to process the image acquired using the first detection wavelength range to identify individual cells and determine the positions of the individual cells, and output the quantitative measurement of the signal obtained from the contrast agent by measuring the signal at the positions of the individual cells and correcting the signal using the background value, the background value being measured at a position corresponding to the position of the individual cells in the image acquired using the second detection wavelength range.

8. The system according to claim 3, wherein the second optical section imaging mode includes stimulated Raman scattering microscopy and acquires the image at a wave number corresponding to the CH2-vibration of lipid molecules of 2,850 cm -1 -1.

9. The system according to claim 8, wherein an image is also acquired at a wave number corresponding to the CH3-vibration of the protein and nucleic acid molecule at 2,930 cm -1 .

10. The system according to claim 1, wherein the contrast agent comprises fluorescein, 5-ALA, BLZ-100, or LUM015.

11. The system according to claim 7, wherein the contrast agent comprises 5-aminolevulinic acid (5-ALA), wherein the first detection wavelength range comprises 640 nm, and the second detection wavelength range comprises wavelengths shorter than 600 nm.

12. The system according to claim 1, wherein the processor is configured to detect individual cells based on image feature size, shape, pattern, intensity, or any combination thereof.

13. The system according to claim 1, wherein the processor runs a supervised machine learning algorithm, an unsupervised machine learning algorithm, a semi-supervised machine learning algorithm, or any combination thereof.

14. The system according to claim 13, wherein the machine learning algorithm is trained using a training dataset that comprises imaging data obtained for archived histopathological tissue samples, imaging data obtained for fresh histopathological tissue samples, or any combination thereof.

15. The system according to claim 14, wherein the training dataset is continuously, periodically, or randomly updated using imaging data obtained by two or more systems deployed at the same or different sites.

16. The system according to claim 1, wherein the processor is configured to determine the total intensity or average intensity of the signal obtained from the contrast agent.

17. The system according to claim 1, wherein the processor is configured to determine whether the signal obtained from the contrast agent for an individual cell is higher than a specified threshold level for contrast-positive cells.

18. The system according to claim 17, wherein the processor is configured to output the total number of contrast-positive cells in the image of the tissue sample, the density of contrast-positive cells in the image of the tissue sample, the percentage of contrast-positive cells in the image of the tissue sample, or any combination thereof.

19. The system according to claim 18, wherein the processor is further configured to output a cellularity fraction based on the image obtained using the second optical sectioning imaging mode.

20. The system according to claim 1, wherein the image of the tissue sample is acquired in vivo.

21. The system according to claim 1, wherein the image of the tissue sample is acquired in vitro.

22. The system according to claim 1, wherein the system is used during surgery to identify a location for performing a biopsy or to determine whether an excision is complete.

23. A method for imaging a tissue sample, the method comprising: a) obtaining a high-resolution optical section image of the distribution of a contrast agent within the tissue sample using a first optical sectioning imaging mode; b) obtaining a high-resolution optical section image of the tissue sample morphology within the same optical focal plane as in (a) within the tissue sample using a second optical sectioning imaging mode; and c) using a processor to process the images, the processor being configured to: process the high-resolution optical section images of the distribution of the contrast agent and / or the high-resolution optical section images of the tissue sample morphology to identify individual cells and determine the positions of the individual cells; (ii) process the high-resolution optical section images of the distribution of the contrast agent to obtain the signal of the contrast agent at the positions of the individual cells; (iii) measure a background value at the positions of the individual cells from the high-resolution optical section images of the distribution of the contrast agent; (iv) correct the signal of the contrast agent at the positions of the individual cells using the background value; and (v) output a quantitative measurement of the signal of the contrast agent.

24. The method according to claim 23, wherein the first optical section imaging mode includes two-photon fluorescence microscopy, confocal fluorescence microscopy, light sheet microscopy or structured illumination microscopy.

25. The method according to claim 23, wherein the second optical section imaging mode includes stimulated Raman scattering microscopy, coherent anti-Stokes Raman scattering microscopy, confocal reflection microscopy, second harmonic generation microscopy or third harmonic generation microscopy.

26. The method according to claim 23, wherein the images obtained using the first optical section imaging mode and the second optical section imaging mode have an axial resolution of less than 10 μm.

27. The method according to claim 23, wherein the images obtained using the first optical section imaging mode and the second optical section imaging mode have a lateral resolution of less than 5 μm.

28. The method according to claim 23, the method further comprising obtaining images using the first optical section imaging mode in two or more detection wavelength ranges.

29. The method according to claim 28, wherein: the two or more detection wavelength ranges include a first detection wavelength range and a second detection wavelength range, wherein the first detection wavelength range includes the emission peak of the contrast agent and the second detection wavelength range does not include the emission peak of the contrast agent; and wherein the processor is further configured to process the images obtained using the first detection wavelength range to identify individual cells and determine the positions of the individual cells, and output the quantitative measurement of the signal obtained from the contrast agent by measuring the signal at the positions of the individual cells and correcting the signal using the background value, the background value being measured at a position corresponding to the position of the individual cells in the images obtained using the second detection wavelength range.

30. The method according to claim 25, wherein the second optical section imaging mode includes stimulated Raman scattering microscopy and acquires the image at a wave number corresponding to the CH2-vibration of lipid molecules of 2,850 cm -1 .

31. The method according to claim 30, wherein an image is also acquired at a wave number corresponding to the CH3-vibration of the protein and nucleic acid molecule at 2,930 cm -1 .

32. The method according to claim 23, wherein the contrast agent includes fluorescein, 5-ALA, BLZ-100 or LUM015.

33. The method according to claim 29, wherein the contrast agent includes 5-aminolevulinic acid (5-ALA), wherein the first detection wavelength range includes 640 nm and the second detection wavelength range includes wavelengths shorter than 600 nm.

34. The method according to claim 23, wherein the processor is configured to detect individual cells based on image feature size, shape, pattern, intensity, or any combination thereof.

35. The method according to claim 23, wherein the processor is configured to run a supervised machine learning algorithm, an unsupervised machine learning algorithm, a semi-supervised machine learning algorithm, or any combination thereof.

36. The method according to claim 35, wherein the machine learning algorithm is trained using a training dataset that includes imaging data obtained for archived histopathological tissue samples, imaging data obtained for fresh histopathological tissue samples, or any combination thereof.

37. The method according to claim 36, wherein the training dataset is continuously, periodically, or randomly updated using imaging data obtained by two or more systems deployed at the same or different sites.

38. The method according to claim 23, wherein the processor is configured to determine the total intensity or average intensity of the signal obtained from the contrast agent.

39. The method according to claim 23, wherein the processor is configured to determine whether the signal obtained from the contrast agent for an individual cell is higher than a specified threshold level for contrast-positive cells.

40. The method according to claim 39, wherein the processor is configured to output the total number of contrast-positive cells in the image of the tissue sample, the density of contrast-positive cells in the image of the tissue sample, the percentage of contrast-positive cells in the image of the tissue sample, or any combination thereof.

41. The method according to claim 40, wherein the processor is configured to further output a cellularity fraction based on the image obtained using the second optical section imaging mode.

42. The method according to claim 23, wherein the image of the tissue sample is acquired in vivo.

43. The method according to claim 23, wherein the image of the tissue sample is acquired in vitro.

44. The method according to claim 23, wherein the method is used during surgery to identify a location for performing a biopsy or to determine whether an excision is complete.

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