Abstract: The disclosure provides example methods determining, non-destructively and in a label-free manner, a level of pluripotency of cell colonies within a cell sample using phase contrast or other microscopic image data taken of the cell sample. A machine learning model is used to determine the locations and extents of nuclei within the image, and the location and extent of cell colonies within the image is also determined. For a given cell colony, a descriptive feature vector is determined based on those of the nuclei that are located within the cell colony, along with the portion of the image that depicts the cell colony (e.g., the shape and size of the portion and/or the brightness or other image information about the portion). The descriptive feature vector is then applied to another machine learning model to predict, for the cell sample, the pluripotency level.

Abstract: The present disclosure provides methods and systems for processing images of three-dimensional biological samples. In particular, the methods apply image processing algorithms to enable segmentation of label-free images of organoids in plates with microwells by segmenting only the samples and ignoring features of the microwells, such as the microwell walls. These methods further including projecting depth-spanning stacks of limited depth-of-field images of the samples into a single image of the sample that can provide in-focus image information about three-dimensional contents of the image. These methods further 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.

Abstract: The present disclosure provides methods and systems for processing images of three-dimensional biological samples. In particular, the methods apply image processing algorithms to enable segmentation of label-free images of organoids in plates with microwells by segmenting only the samples and ignoring features of the microwells, such as the microwell walls. These methods further including projecting depth-spanning stacks of limited depth-of-field images of the samples into a single image of the sample that can provide in-focus image information about three-dimensional contents of the image. These methods further 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.

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: 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.