Abstract: A system and process use artificial intelligence to evaluate the toxicity of drugs on cells. In some embodiments, a convolutional neural network is trained to identify features in cells and thereafter identify when structural changes in cells are signs of damage from exposure to a drug. Some embodiments use a 2-class deep neural network, comparing drug-treated cells to controls, to learn which images may show signs of toxicity as a result of the drug. In some applications, the system may capture images from a time-lapse experiment to determine from the cell cultures how a drug affects a cell type over time.

Abstract: A method and system for characterizing a cell population including a set of cell subpopulations, the method comprising: at a processing system, receiving image data corresponding to a set of images of the cell population captured at a set of time points; at the processing system, generating an analysis based upon processing the set of images according to: a cell stage classification module configured to automatically identify a cell class of each of the set of cell subpopulations, and a cell graph representation module configured to characterize geometric and spatial features of the set of cell subpopulations; from the analysis, determining a set of parameters characterizing the cell population; and generating an indication of quality of the cell population based upon a comparison between at least one parameter of the set of parameters and a set of reference values.

Abstract: A method and system for characterizing cell motion comprising: receiving image data corresponding to a set of images of a cell culture captured at a set of time points; segmenting, from at least one image of the set of images, a cell subpopulation from the cell culture; determining a resting signal for the cell subpopulation; determining a single-peak motion signal based upon the set of images, the set of time points, and the resting signal; detrending the single-peak motion signal of the cell subpopulation based upon the resting signal; determining values of a set of motion features of the cell subpopulation, thereby characterizing cell motion; and clustering the cell subpopulation with at least one other cell subpopulation based upon at least one of the single-peak motion signal and a value of the set of motion features.

Abstract: A method and system for characterizing cell motion comprising: receiving image data corresponding to a set of images of a cell culture captured at a set of time points; segmenting, from at least one image of the set of images, a cell subpopulation from the cell culture; determining a resting signal for the cell subpopulation; determining a single-peak motion signal based upon the set of images, the set of time points, and the resting signal; detrending the single-peak motion signal of the cell subpopulation based upon the resting signal; determining values of a set of motion features of the cell subpopulation, thereby characterizing cell motion; and clustering the cell subpopulation with at least one other cell subpopulation based upon at least one of the single-peak motion signal and a value of the set of motion features.

Abstract: A method and system for characterizing cell motion comprising: receiving image data corresponding to a set of images of a cell culture captured at a set of time points; segmenting, from at least one image of the set of images, a cell subpopulation from the cell culture; determining a resting signal for the cell subpopulation; determining a single-peak motion signal based upon the set of images, the set of time points, and the resting signal; detrending the single-peak motion signal of the cell subpopulation based upon the resting signal; determining values of a set of motion features of the cell subpopulation, thereby characterizing cell motion; and clustering the cell subpopulation with at least one other cell subpopulation based upon at least one of the single-peak motion signal and a value of the set of motion features.

Abstract: A method and system for characterizing cell motion comprising: receiving image data corresponding to a set of images of a cell culture captured at a set of time points; segmenting, from at least one image of the set of images, a cell subpopulation from the cell culture; determining a resting signal for the cell subpopulation; determining a single-peak motion signal based upon the set of images, the set of time points, and the resting signal; detrending the single-peak motion signal of the cell subpopulation based upon the resting signal; determining values of a set of motion features of the cell subpopulation, thereby characterizing cell motion; and clustering the cell subpopulation with at least one other cell subpopulation based upon at least one of the single-peak motion signal and a value of the set of motion features.

Abstract: A method and system for characterizing a cell population including a set of cell subpopulations, the method comprising: at a processing system, receiving image data corresponding to a set of images of the cell population captured at a set of time points; at the processing system, generating an analysis based upon processing the set of images according to: a cell stage classification module configured to automatically identify a cell class of each of the set of cell subpopulations, and a cell graph representation module configured to characterize geometric and spatial features of the set of cell subpopulations; from the analysis, determining a set of parameters characterizing the cell population; and generating an indication of quality of the cell population based upon a comparison between at least one parameter of the set of parameters and a set of reference values.