Abstract: Systems and methods for autofocus using artificial intelligence include (i) capturing a plurality of monochrome images over a nominal focus range, (ii) identifying one or more connected components within each monochrome image, (iii) sorting the identified connected components based on a number of pixels associated with each connected component, (iv) generating a focus quality estimate of at least a portion of the sorted connected components using a machine learning module, and (iv) calculating a target focus position based on the focus quality estimate of the evaluated connected components. The calculated target focus position can be used to perform cell counting using artificial intelligence, such as by (i) generating a seed likelihood image and a whole cell likelihood image based on output—a convolutional neural network and (ii) generating a mask indicative quantity and/or pixel locations of objects based on the seed likelihood image.

Abstract: Systems and methods for identifying clumps (or groupings) of cells in cell counting systems. One example method executed by an electronic processing device for an image processing system includes receiving an image, captured by an imaging device, of a sample having a plurality of cells. The image includes labeling data for each of the plurality of cells. The method includes applying filters, determined based on the labeling data, to the image, processing the image to identify a plurality of groupings of the plurality of cells, and fitting an ellipse around each of the groupings by determining ellipse data for each of the ellipses. Each respective grouping of the plurality of groupings includes at least two cells having a same viability whose cell membranes are touching at least one other cell in the respective grouping.

Abstract: Disclosed herein are machine learning-based particle classification systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a particle classification system may include: an electronic processing device configured to: receive, from an imaging flow cytometer instrument, a test set comprising unlabeled data to classify; pool the test set and a training set into a concatenated dataset comprising a plurality of parameters, wherein the training set comprises labeled data; normalize the concatenated dataset by bringing the variance to one for each of the plurality of parameters; non-linearly reduce a dimensionality of the normalized concatenated dataset to a reduced dimension space; compute classification parameters by classifying the unlabeled data from the reduced dimension space using the labeled data from the reduced dimension space; and provide the classification parameters for further processing.

Abstract: Examples described herein provide systems and methods for quantifying cells. An example method includes receiving at least one image, improving a contrast of the at least one image to generate a contrast image, and performing a fit operation on the contrast image to generate a processed image. The method includes applying a filter to the processed image to generate a filtered image, identifying cells within the filtered image, and providing an output image including an indication of the cells.

Abstract: Embodiments relate to systems and methods for sample image capture using integrated control. A digital microscope or other imaging device can be associated with a sample chamber containing cell, tissue, or other sample material. The chamber can be configured to operate using a variety of environmental variables, including gas concentration, temperature, humidity, and others. The imaging device can be configured to operate using a variety of imaging variables, including magnification, focal length, illumination, and others. A central system control module can be used to configure the settings of those hardware elements, as well as others, to set up and carry out an image capture event. The system control module can be operated to control the physical, optical, chemical, and/or other parameters of the overall imaging environment from one central control point. The variables used to produce the image capture can be configured to dynamically variable during the media capture event.

Abstract: Systems and methods for autofocus using artificial intelligence include (i) capturing a plurality of monochrome images over a nominal focus range, (ii) identifying one or more connected components within each monochrome image, (iii) sorting the identified connected components based on a number of pixels associated with each connected component, (iv) generating a focus quality estimate of at least a portion of the sorted connected components using a machine learning module, and (iv) calculating a target focus position based on the focus quality estimate of the evaluated connected components. The calculated target focus position can be used to perform cell counting using artificial intelligence, such as by (i) generating a seed likelihood image and a whole cell likelihood image based on output—a convolutional neural network and (ii) generating a mask indicative quantity and/or pixel locations of objects based on the seed likelihood image.

Abstract: A method for calibrating an imaging system can include at least the following method acts: illuminating a sample through a pinhole mask using an excitation light; capturing an image of the sample using a sensor; converting the image into data; in a processing module: filtering the data using a known spacing of pinholes in the pinhole mask to obtain filtered data that corresponds to the known spacing, using a threshold to identify regions of the filtered data that are bright enough to be associated with a pinhole, calculating the centroids of the regions, and fitting a known pattern for the pinhole mask to the regions in order to identify the best fit data for the filtered data; and storing, in a storage medium, the best fit data for use in a subsequent confocal capture routine.

Abstract: A method for generating a composite image obtained in a fluorescence imaging system, comprising: determining the physical locations of a pinhole mask relative to a sample required to construct a full composite confocal image; generating in a control module a randomized order for the determined physical locations; moving the photomask or the sample to the determined physical locations in the randomized order under control of the control module and using a translation stage; illuminating the sample through the photomask using an excitation light; capturing a plurality of images at each of the physical locations using a sensor in order to generate a set of data points; using the randomized order to generate a composite image based on the set of data points and measuring the brightness of at least some of the data points; and adjusting the brightness of some of the set of data points.

Abstract: Embodiments relate to systems and methods for sample image capture using integrated control. A digital microscope or other imaging device can be associated with a sample chamber containing cell, tissue, or other sample material. The chamber can be configured to operate using a variety of environmental variables, including gas concentration, temperature, humidity, and others. The imaging device can be configured to operate using a variety of imaging variables, including magnification, focal length, illumination, and others. A central system control module can be used to configure the settings of those hardware elements, as well as others, to set up and carry out an image capture event. The system control module can be operated to control the physical, optical, chemical, and/or other parameters of the overall imaging environment from one central control point. The variables used to produce the image capture can be configured to dynamically variable during the media capture event.

Abstract: A method for calibrating an imaging system, comprising: illuminating a sample through a pinhole mask using an excitation light; capturing an image of the sample using a sensor; converting the image into data; in a processing module: filtering the data using known spacing of pinholes in the pinhole mask to obtain data that corresponds to the spacing, using a threshold to identify regions of the remaining data that are bright enough to be associated with a pinhole, calculating the centroids of the regions, and fitting a known pattern for the pinhole mask to the regions in order to identify the best fit for the data; and storing, in a storage medium, the best fit data for use in a subsequent confocal capture routine.

Abstract: A method for generating a composite image obtained in a fluorescence imaging system, comprising: determining the physical locations of a pinhole mask relative to a sample required to construct a full composite confocal image; generating in a control module a randomized order for the determined physical locations; moving the photomask or the sample to the determined physical locations in the randomized order under control of the control module and using a translation stage; illuminating the sample through the photomask using an excitation light; capturing a plurality of images at each of the physical locations using a sensor in order to generate a set of data points; using the randomized order to generate a composite image based on the set of data points and measuring the brightness of at least some of the data points; and adjusting the brightness of some of the set of data points.

Abstract: Embodiments relate to systems and methods for sample image capture using integrated control. A digital microscope or other imaging device can be associated with a sample chamber containing cell, tissue, or other sample material. The chamber can be configured to operate using a variety of environmental variables, including gas concentration, temperature, humidity, and others. The imaging device can be configured to operate using a variety of imaging variables, including magnification, focal length, illumination, and others. A central system control module can be used to configure the settings of those hardware elements, as well as others, to set up and carry out an image capture event. The system control module can be operated to control the physical, optical, chemical, and/or other parameters of the overall imaging environment from one central control point. The variables used to produce the image capture can be configured to dynamically variable during the media capture event.

Abstract: A high speed system for locating and decoding glyphs on documents is disclosed. The system includes acquiring one or more images of a document containing a glyph. One-dimensional projections of the images are correlated against a reference function to locate the glyph in the images. The position of the glyph is refined by correlating against a kernel designed to have a maximum response when aligned over a corner of the glyph. Symbols in the glyph are decoded utilizing a kernel which generates a positive response for one symbol type and a negative response for the other.

Abstract: A high speed system for locating and decoding glyphs on documents is disclosed. The system includes acquiring one or more images of a document containing a glyph. One-dimensional projections of the images are correlated against a reference function to locate the glyph in the images. The position of the glyph is refined by correlating against a kernel designed to have a maximum response when aligned over a corner of the glyph. Symbols in the glyph are decoded utilizing a kernel which generates a positive response for one symbol type and a negative response for the other.