Abstract: A high-content imaging system includes a stage, a controller, a machine learning system, and an image generator. The controller receives a request including an output imaging configuration and in response the controller: (1) selects a training model associated with the output imaging configuration, (2) determines an input imaging configuration associated with the training model, and (3) configures the high-content imaging system in accordance with the input imaging configuration. The machine learning system is configured using the training model so when the machine learning system is presented with an image acquired using the input imaging configuration, the machine learning system generates an output image in accordance with the output imaging configuration. The image generator generates an image of a sample on the stage and provides the generated image to the machine learning system and, in response, the machine learning system generates an output image in accordance with the output imaging configuration.

Abstract: A high-content imaging system includes a stage, a controller, a machine learning system, and an image generator. The controller receives a request including an output imaging configuration and in response the controller: (1) selects a training model associated with the output imaging configuration, (2) determines an input imaging configuration associated with the training model, and (3) configures the high-content imaging system in accordance with the input imaging configuration. The machine learning system is configured using the training model so when the machine learning system is presented with an image acquired using the input imaging configuration, the machine learning system generates an output image in accordance with the output imaging configuration. The image generator generates an image of a sample on the stage and provides the generated image to the machine learning system and, in response, the machine learning system generates an output image in accordance with the output imaging configuration.

Abstract: A method of generating in-focus images of measurement locations of a sample holder in a microscopy imaging system is provided. A camera array is positioned at a first distance from the sample holder. A first image of a measurement location is acquired using an imaging device disposed on the camera array. A candidate output image associated with the imaging device is developed in accordance with the first image. The camera array is positioned at a second distance from the sample holder and a second image of the measurement location is acquired using the imaging device. A portion of the candidate output image is updated in accordance with a portion of the second image in accordance with a selection criterion. The updated candidate image is transmitted.

Abstract: A computer-implemented method and system perform efficient and accurate fiber morphology analysis of fiber-containing biological samples. The images of objects comprising fiber and/or branching structure are obtained by camera and/or other imaging devices. Initial tracing seeds are obtained by variable modules. The adaptive threshold method is utilized for identifying tracing seeds candidates by local Principal Component Analysis (PCA) calculation and sorting out the seeds with low and high score. Each single fiber segment of interest is traced repeatedly utilizing PCA fitting calculation, and individual fiber segments are assembled by performing crossover matching calculation.

Abstract: A method of generating in-focus images of measurement locations of a sample holder in a microscopy imaging system is provided. A camera array is positioned at a first distance from the sample holder. A first image of a measurement location is acquired using an imaging device disposed on the camera array. A candidate output image associated with the imaging device is developed in accordance with the first image. The camera array is positioned at a second distance from the sample holder and a second image of the measurement location is acquired using the imaging device. A portion of the candidate output image is updated in accordance with a portion of the second image in accordance with a selection criterion. The updated candidate image is transmitted.

Abstract: A computer-implemented method and system of fiber analysis and reconstruction of a fiber-containing biological sample perform efficient and accurate analysis of fiber morphology. The images of objects comprising fiber and/or branching structure are obtained by camera and/or other imaging devices. Variable modules are operable to obtain initial tracing seeds via adaptive threshold method for identifying tracing seeds candidates by local Principal Component Analysis (PCA) calculation and sorting out the seeds with low and high score. Each single fiber segment of interest is traced repeatedly utilizing PCA fitting calculation, and individual fiber segments are assembled by performing crossover matching calculation.

Abstract: A system and method of determining a focal position for an objective positioned at a measurement location of a sample holder in a microscopy imaging system are provided. The objective is moved to a position relative to the sample holder that corresponds to a distance between the objective and the sample holder. The sample holder has a conditioned upper surface. A focusing light beam is projected onto the sample holder when the objective is located at the position, and the objective focuses the focusing light beam on the sample holder. A reflected light beam resulting from reflection of the focusing light beam off the conditioned upper surface is observed. The focal position for the objective is determined based on the reflected light beam such that the objective produces an in focus image of a microscopy sample when the objective is located at the focal position.

Abstract: A system and method of determining a focal position for an objective positioned at a measurement location of a sample holder in a microscopy imaging system are provided. The objective is moved to a position relative to the sample holder that corresponds to a distance between the objective and the sample holder. The sample holder has a conditioned upper surface. A focusing light beam is projected onto the sample holder when the objective is located at the position, and the objective focuses the focusing light beam on the sample holder. A reflected light beam resulting from reflection of the focusing light beam off the conditioned upper surface is observed. The focal position for the objective is determined based on the reflected light beam such that the objective produces an in focus image of a microscopy sample when the objective is located at the focal position.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing an integrated data visualization interface for multi-dimensional microscopy image and measurement data are disclosed. The integrated data visualization interface utilizes the known dimensional relational relationships between the images to automatically provide synchronized and coordinated visualization and manipulations across multiple related images and measurement data in multiple dataset viewer components (e.g., image grids, browsable image strips, tables, and graphs). Other features of the dataset viewer components are also disclosed.

Abstract: Methods and systems for counting nuclei for cells in a cell-containing sample are disclosed, such as carried out on a computer. The methods comprise: receiving a raw image of the cell-containing sample; transforming the raw image into a segmented image comprising one or more nuclei clusters. The methods further comprise: for each of the one or more nuclei clusters, obtaining a convex hull of the nuclei cluster; locating any indentations on the nuclei cluster by comparing the nuclei cluster to the convex hull of the nuclei cluster; calculating a first nuclei count based on a tally of the indentations; and assigning the nuclei cluster to a cell among the plurality of cells. The methods further comprise: calculating a second nuclei count for each cell by totaling the first nuclei counts of its constituent nuclei clusters; and presenting a result based on the second nuclei count for at least one of the plurality of cells.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing an integrated data visualization interface for multi-dimensional microscopy image and measurement data are disclosed. The integrated data visualization interface utilizes the known dimensional relational relationships between the images to automatically provide synchronized and coordinated visualization and manipulations across multiple related images and measurement data in multiple dataset viewer components (e.g., image grids, browsable image strips, tables, and graphs). Other features of the dataset viewer components are also disclosed.

Abstract: Methods and systems for counting nuclei for cells in a cell-containing sample are disclosed, such as carried out on a computer. The methods comprise: receiving a raw image of the cell-containing sample; transforming the raw image into a segmented image comprising one or more nuclei clusters. The methods further comprise: for each of the one or more nuclei clusters, obtaining a convex hull of the nuclei cluster; locating any indentations on the nuclei cluster by comparing the nuclei cluster to the convex hull of the nuclei cluster; calculating a first nuclei count based on a tally of the indentations; and assigning the nuclei cluster to a cell among the plurality of cells. The methods further comprise: calculating a second nuclei count for each cell by totaling the first nuclei counts of its constituent nuclei clusters; and presenting a result based on the second nuclei count for at least one of the plurality of cells.