Method and system for characterizing cell populations

1. A method 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 stage of each of the set of cell subpopulations, wherein processing the set of images comprises:

a partitioning each image in the set of images into a set of pixel subsets, wherein each pixel subset of the pixel subsets corresponds to a cell subpopulation of the set of cell subpopulations,

producing a set of feature vectors corresponding to the set of pixel subsets for each image, wherein producing comprises, for each pixel subset of the set of pixel subsets:

generating a local binary pattern histogram associated with the cell subpopulation corresponding to the pixel subset,

generating a pixel intensity histogram associated with the cell subpopulation corresponding to the pixel subset, and

generating a feature vector based upon processing of the local binary pattern histogram and the pixel intensity histogram, wherein the set of feature vectors comprises the feature vector; and

training a machine learning classifier, with a training dataset, to identify each of a set of cell stages of the cell population captured in the set of images, using the set of feature vectors comprising the feature vectors generated based upon processing the local binary pattern histograms and the pixel intensity histograms of the set of pixel subsets;

from the analysis, determining a set of parameters characterizing the cell population; and

at the processing system, generating an indication of a characteristic of the cell population based upon a comparison between at least one parameter of the set of parameters and a set of reference values,

wherein generating the pixel intensity histogram comprises generating the pixel intensity histogram based on the frequency of 1's and 0's from the local binary pattern histogram, and

wherein generating the feature vector based upon processing of the local binary pattern histogram and the pixel intensity histogram comprises concatenating the local binary pattern histogram and the pixel intensity histogram associated with the cell subpopulation corresponding to the pixel subset.

2. The method of claim 1 , wherein receiving image data comprises receiving a set of phase contrast images, for each of a set of locations spanning the cell population at each of the set of time points.

3. The method of claim 1 , wherein processing the set of images according to the cell stage classification module comprises enabling identification of at east one of: a single-cell stage, a medium-compaction stage, a full-compaction stage, a dead cell stage, a differentiated cell stage, and a debris stage exhibited by cells of the cell population at the cell stage classification module.

4. The method of claim 1 wherein generating the analysis further comprises processing the set of images according to a cell graph representation module, including:

at a cell cluster segmentation module, segmenting, from at least one image of the set of images, a set of regions corresponding to the set of cell subpopulations of the cell population, each region defined by a set of nodes and a set of edges, and

determining a set of parameters characterizing the cell population based upon an analysis of the set of nodes and the set of edges of at least one of the set of regions.

5. The method of claim 4 , wherein determining the set of parameters comprises determining parameters indicative of at least one of: degree of cell compaction, cell doubling time, sensitivity to culture media change, and colony border spikiness based upon outputs of at least one of the cell stage classification module and the cell graph representation module.

6. The method of claim 1 , further comprising generating a notification based upon the indication and providing the indication to an entity associated with the cell population, at a mobile device of the entity.

7. The method of claim 1 , further comprising using an output of the cell stage classification module to determine an expansion rate-related parameter based on a set of weighted areas by density, including:

for each image in the set of images and for each of the set of cell stages, determining an area occupied cells exhibiting the cell stage, and weighting the area by a density corresponding to the cell stage, wherein the set of weighted areas by density comprises the weighted area.

8. The method of claim 1 , further comprising using an output of the cell stage classification module to determine a compaction-related parameter, derived from an amount of cells of the cell population exhibiting a compaction stage across a subset of the set of images.

9. The method of claim 1 , further comprising using an output of the cell stage classification module to determine a morphology-related parameter indicative of colony border spikiness of the cell population, based upon a quantification of weights associated with the set of edges having values below a threshold value.

10. The method of claim 1 , wherein the indication of the characteristic of the cell population comprises at least one of: quality, prevalence of differentiated cells, differentiation stage, cell compaction, and cell border spikiness.