Abstract: The disclosure provides example embodiments for automatically or semi-automatically classifying cells in microscopic images of biological samples. These embodiments include methods for selecting training sets for the development of classifier models. The disclosed selection embodiments can allow for the re-training of classifier models using training examples that have been subjected to the same or similar incubation conditions as target samples. These selection embodiments can reduce the amount of human effort required to specify the training examples. The disclosed embodiments also include the classification of individual cells based on metrics determined for the cells using phase contrast imagery and defocused brightfield imagery. These metrics can include size, shape, texture, and intensity-based metrics. These metrics are determined based on segmentation of the underlying imagery.

Abstract: A method of analyzing images of a biological specimen using a computational model is described, the method including processing a cell image of the biological specimen and a phase contrast image of the biological specimen using the computational model to generate an output data. The cell image is a composite of a first brightfield image of the biological specimen at a first focal plane and a second brightfield image of the biological specimen at a second focal plane. The method also includes performing a comparison of the output data and a reference data and refining the computational model based on the comparison of the output data and the reference data. The method also includes thereafter processing additional image pairs according to the computational model to further refine the computational model based on comparisons of additional output data generated by the computational model to additional reference data.

Abstract: Methods are provided to project depth-spanning stacks of limited depth-of-field images of a sample into a single image of the sample that can provide in-focus image information about three-dimensional contents of the image. These methods include applying filters to the stacks of images in order to identify pixels within each image that have been captured in focus. These in-focus pixels are then combined to provide the single image of the sample. Filtering of such image stacks can also allow for the determination of depth maps or other geometric information about contents of the sample. Such depth information can also be used to inform segmentation of images of the sample, e.g., by further dividing identified regions that correspond to the contents of the sample at multiple different depths.

Abstract: Systems and methods are provided for automatically imaging and analyzing cell samples in an incubator. An actuated microscope operates to generate images of samples within wells of a sample container across days, weeks, or months. A plurality of images is generated for each scan of a particular well, and the images within such a scan are used to image and analysis metabolically active cells in the well. Tins analysis includes generating a “range image” by subtracting the minimum intensity value, across the scan, for each pixel from the maximum intensity value. This range image thus emphasizes cells or portions of cells that exhibit changes in activity over a scan period (e.g., neurons, myocytes, cardiomyocytes) while de-emphasizing regions that exhibit consistently high intensities when images (e.g., regions exhibiting a great deal of autofluorescence unrelated to cell activity).

Abstract: Methods are provided to project depth-spanning stacks of limited depth-of-field images of a sample into a single image of the sample that can provide in-focus image information about three-dimensional contents of the image. These methods include applying filters to the stacks of images in order to identify pixels within each image that have been captured in focus. These in-focus pixels are then combined to provide the single image of the sample. Filtering of such image stacks can also allow for the determination of depth maps or other geometric information about contents of the sample. Such depth information can also be used to inform segmentation of images of the sample, e.g., by further dividing identified regions that correspond to the contents of the sample at multiple different depths.

Abstract: Systems and methods are provided for automatically imaging and analyzing cell samples in an incubator. An actuated microscope operates to generate images of samples within wells of a sample container across days, weeks, or months. A plurality of images is generated for each scan of a particular well, and the images within such a scan are used to image and analysis metabolically active cells in the well. Tins analysis includes generating a “range image” by subtracting the minimum intensity value, across the scan, for each pixel from the maximum intensity value. This range image thus emphasizes cells or portions of cells that exhibit changes in activity over a scan period (e.g., neurons, myocytes, cardiomyocytes) while de-emphasizing regions that exhibit consistently high intensities when images (e.g., regions exhibiting a great deal of autofluorescence unrelated to cell activity).

Abstract: Systems and methods are provided for automatically imaging and analyzing cell samples in an incubator. An actuated microscope operates to generate images of samples within wells of a sample container across days, weeks, or months. A plurality of images is generated for each scan of a particular well, and the images within such a scan are used to image and analysis metabolically active cells in the well. Tins analysis includes generating a “range image” by subtracting the minimum intensity value, across the scan, for each pixel from the maximum intensity value. This range image thus emphasizes cells or portions of cells that exhibit changes in activity over a scan period (e.g., neurons, myocytes, cardiomyocytes) while de-emphasizing regions that exhibit consistently high intensities when images (e.g., regions exhibiting a great deal of autofluorescence unrelated to cell activity).

Abstract: Methods are provided to project depth-spanning stacks of limited depth-of-field images of a sample into a single image of the sample that can provide in-focus image information about three-dimensional contents of the image. These methods include applying filters to the stacks of images in order to identify pixels within each image that have been captured in focus. These in-focus pixels are then combined to provide the single image of the sample. Filtering of such image stacks can also allow for the determination of depth maps or other geometric information about contents of the sample. Such depth information can also be used to inform segmentation of images of the sample, e.g., by further dividing identified regions that correspond to the contents of the sample at multiple different depths.

Abstract: A method of analyzing images of a biological specimen using a computational model is described, the method including processing a cell image of the biological specimen and a phase contrast image of the biological specimen using the computational model to generate an output data. The cell image is a composite of a first brightfield image of the biological specimen at a first focal plane and a second brightfield image of the biological specimen at a second focal plane. The method also includes performing a comparison of the output data and a reference data and refining the computational model based on the comparison of the output data and the reference data. The method also includes thereafter processing additional image pairs according to the computational model to further refine the computational model based on comparisons of additional output data generated by the computational model to additional reference data.

Abstract: The disclosure provides example embodiments for automatically or semi-automatically classifying cells in microscopic images of biological samples. These embodiments include methods for selecting training sets for the development of classifier models. The disclosed selection embodiments can allow for the re-training of classifier models using training examples that have been subjected to the same or similar incubation conditions as target samples. These selection embodiments can reduce the amount of human effort required to specify the training examples. The disclosed embodiments also include the classification of individual cells based on metrics determined for the cells using phase contrast imagery and defocused brightfield imagery. These metrics can include size, shape, texture, and intensity-based metrics. These metrics are determined based on segmentation of the underlying imagery.

Abstract: Systems and methods are provided for automatically imaging and analyzing cell samples in an incubator. An actuated microscope operates to generate images of samples within wells of a sample container across days, weeks, or months. A plurality of images is generated for each scan of a particular well, and the images within such a scan are used to image and analysis metabolically active cells in the well. Tins analysis includes generating a “range image” by subtracting the minimum intensity value, across the scan, for each pixel from the maximum intensity value. This range image thus emphasizes cells or portions of cells that exhibit changes in activity over a scan period (e.g., neurons, myocytes, cardiomyocytes) while de-emphasizing regions that exhibit consistently high intensities when images (e.g., regions exhibiting a great deal of autofluorescence unrelated to cell activity).

Abstract: The disclosure provides example methods that include a processor: (a) generating at least one phase contrast image of a biological specimen comprising one or more cells centered around a focal plane for the biological specimen; (b) generating a confluence mask in the form of a binary image based on the at least one phase contrast image; (c) receiving a first brightfield image of the biological specimen at a defocusing distance above the focal plane and a second brightfield image of the biological specimen at the defocusing distance below the focal plane; (d) generating a cell image of the biological specimen based on the first and second brightfield image; (e) generating a seed mask based on the cell image and the phase contrast image; and (f) generating an image of the biological specimen showing a cell-by-cell segmentation mask based on the seed mask and the confluence mask.

Abstract: Systems and methods are provided for microscopically and fluorescently imaging cell-bearing biological samples or other samples of interest. A microscope objective or other optical elements that exhibits chromatic aberration can be used to obtain images of fluorophores or other contrast agents at different wavelengths. The obtained images are then used to correct each other, e.g., to remove artifacts in an image of a shorter-wavelength fluorophore that are caused by cross-talk from a longer-wavelength fluorophore. A longer-wavelength image, taken at a focal distance corresponding to the shorter-wavelength fluorophore, is taken and used to subtract the activity of the longer-wavelength fluorophore in the shorter-wavelength image. The longer-wavelength image may be taken using a microscope set to the shorter-wavelength focal distance.

Abstract: The disclosure provides example methods that include a processor: (a) generating at least one phase contrast image of a biological specimen comprising one or more cells centered around a focal plane for the biological specimen; (b) generating a confluence mask in the form of a binary image based on the at least one phase contrast image; (c) receiving a first brightfield image of the biological specimen at a defocusing distance above the focal plane and a second brightfield image of the biological specimen at the defocusing distance below the focal plane; (d) generating a cell image of the biological specimen based on the first and second brightfield image; (e) generating a seed mask based on the cell image and the phase contrast image; and (f) generating an image of the biological specimen showing a cell-by-cell segmentation mask based on the seed mask and the confluence mask.

Abstract: Systems and methods are provided for microscopically and fluorescently imaging cell-bearing biological samples or other samples of interest. A microscope objective or other optical elements that exhibits chromatic aberration can be used to obtain images of fluorophores or other contrast agents at different wavelengths. The obtained images are then used to correct each other, e.g., to remove artifacts in an image of a shorter-wavelength fluorophore that are caused by cross-talk from a longer-wavelength fluorophore. A longer-wavelength image, taken at a focal distance corresponding to the shorter-wavelength fluorophore, is taken and used to subtract the activity of the longer-wavelength fluorophore in the shorter-wavelength image. The longer-wavelength image may be taken using a microscope set to the shorter-wavelength focal distance.

Abstract: The present invention provides an object identification system including at least one processor; a light source coupled to the at least one processor and configured to emit light towards a retroreflective object and a non-retroreflective object; a first sensor coupled to the at least one processor, the first sensor configured to detect light having a first polarization orientation; and a second sensor coupled to the at least one processor, the second sensor configured to detect light having a second polarization orientation substantially orthogonal to the first polarization orientation.