Abstract: A method is disclosed for analyzing a thin tissue sample and adapted to be supported on a slide. The tissue sample may be placed on a slide and exposed to one or more different exogenous fluorophores excitable in a range of about 300 nm-200 nm, and having a useful emission band from about 350 nm-900 nm, and including one or more fluorescent dyes or fluorescently labeled molecular probes that accumulate in tissue or cellular components. The fluorophores may be excited with a first wavelength of UV light between about 200 nm-290 nm. An optical system collects emissions from the fluorophores at a second wavelength, different from the first wavelength, which are generated in response to the first wavelength of UV light, to produce an image for analysis.

Abstract: A method is disclosed for analyzing a thin tissue sample and adapted to be supported on a slide. The tissue sample may be placed on a slide and exposed to one or more different exogenous fluorophores excitable in a range of about 300 nm-200 nm, and having a useful emission band from about 350 nm-900 nm, and including one or more fluorescent dyes or fluorescently labeled molecular probes that accumulate in tissue or cellular components. The fluorophores may be excited with a first wavelength of UV light between about 200 nm-290 nm. An optical system collects emissions from the fluorophores at a second wavelength, different from the first wavelength, which are generated in response to the first wavelength of UV light, to produce an image for analysis.

Abstract: The present disclosure relates to a method for analyzing tissue specimens. In one implementation the method involves obtaining a tissue sample and exposing the sample to one or more fluorophores as contrast agents to enhance contrast of subcellular compartments of the tissue sample. The tissue sample is illuminated by an ultraviolet (UV) light having a wavelength between about 200 nm to about 400 nm, with the wavelength being selected to result in penetration to only a specified depth below a surface of the tissue sample. Inter-image operations between images acquired under different imaging parameters allow for improvement of the image quality via removal of unwanted image components. A microscope may be used to image the tissue sample and provide the image to an image acquisition system that makes use of a camera. The image acquisition system may create a corresponding image that is transmitted to a display system for processing and display.

Abstract: Apparatus and methods are provided for the imaging of structures in deep tissue within biological specimens, using spectral imaging to provide highly sensitive detection. By acquiring data that provides a plurality of images of the sample with different spectral weightings, and subsequent spectral analysis, light emission from a target compound is separated from autofluorescence in the sample. With the autofluorescence reduced or eliminated, an improved measurement of the target compound is obtained.

Abstract: The present disclosure relates to a method for analyzing tissue specimens. In one implementation the method involves obtaining a tissue sample and exposing the sample to one or more fluorophores as contrast agents to enhance contrast of subcellular compartments of the tissue sample. The tissue sample is illuminated by an ultraviolet (UV) light having a wavelength between about 200 nm to about 400 nm, with the wavelength being selected to result in penetration to only a specified depth below a surface of the tissue sample. Inter-image operations between images acquired under different imaging parameters allow for improvement of the image quality via removal of unwanted image components. A microscope may be used to image the tissue sample and provide the image to an image acquisition system that makes use of a camera. The image acquisition system may create a corresponding image that is transmitted to a display system for processing and display.

Abstract: Disclosed herein are methods and apparatus for obtaining at least one non-birefringence image and at least one birefringence image of a stained sample, and classifying regions of the stained sample into a plurality of classes based on the at least one non-birefringence image and the at least one birefringence image.

Abstract: Methods and systems are disclosed that include: applying an immunohistochemical stain, eosin, and a counterstain to a sample; obtaining a plurality of images of the sample, each of the plurality of images corresponding to radiation from the sample in a different wavelength band; decomposing the plurality of images of the sample to obtain component images corresponding to the immunohistochemical stain, eosin, and the counterstain; and generating a sample image based on the component images, where the sample image includes contributions from the counterstain and from one of the immunohistochemical stain and eosin, and substantially not from the other of the immunohistochemical stain and eosin.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: Apparatus and methods are provided for the imaging of structures in deep tissue within biological specimens, using spectral imaging to provide highly sensitive detection. By acquiring data that provides a plurality of images of the sample with different spectral weightings, and subsequent spectral analysis, light emission from a target compound is separated from autofluorescence in the sample. With the autofluorescence reduced or eliminated, an improved measurement of the target compound is obtained.

Abstract: Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.

Abstract: Apparatus and methods are provided for the imaging of structures in deep tissue within biological specimens, using spectral imaging to provide highly sensitive detection. By acquiring data that provides a plurality of images of the sample with different spectral weightings, and subsequent spectral analysis, light emission from a target compound is separated from autofluorescence in the sample. With the autofluorescence reduced or eliminated, an improved measurement of the target compound is obtained.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: Methods are disclosed that include: (a) applying a first stain to a first sample having a plurality of regions, where the first stain selectively binds to only a first subset of the regions of the first sample; (b) applying a second stain to the first sample, where the second stain binds to a second set of regions of the first sample; (c) obtaining an image of the first sample, and analyzing the image to obtain a first component image corresponding substantially only to spectral contributions from the first stain, and a second component image corresponding substantially only to spectral contributions from the second stain; and (d) training a classifier to identify regions of a second sample based on information derived from the first and second component images, the identified regions corresponding to the first subset of regions of the first sample.

Abstract: Methods are disclosed that include: (a) applying a first stain to a first sample having a plurality of regions, where the first stain selectively binds to only a first subset of the regions of the first sample; (b) applying a second stain to the first sample, where the second stain binds to a second set of regions of the first sample; (c) obtaining an image of the first sample, and analyzing the image to obtain a first component image corresponding substantially only to spectral contributions from the first stain, and a second component image corresponding substantially only to spectral contributions from the second stain; and (d) training a classifier to identify regions of a second sample based on information derived from the first and second component images, the identified regions corresponding to the first subset of regions of the first sample.

Abstract: Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.