Abstract: A method of facilitating a growth assessment for a cell line includes generating an image of a well that contains a medium that was inoculated with at least one cell of the cell line. The method also includes generating a down-sampled segmentation map comprising pixels that indicate an inferred presence or absence of a cell colony in corresponding portions of the well image. Generating the down-sampled segmentation map includes inputting the well image to a fully convolutional neural network having a plurality of convolutional layers. The method also includes (i) determining, by inputting colony size information (including the down-sampled segmentation map and/or a pixel count derived therefrom) to a cell growth assessment algorithm, a growth classification or score for the cell line, and causing a display of the growth classification or score, and/or (II) causing a display of the colony size information to facilitate a manual cell growth assessment.

Abstract: A method for facilitating selection of cell lines to advance to a next stage of cell line screening includes receiving first attribute values for the candidate cell lines measured using an opto-electronic cell line generation and analysis system, and acquiring second attribute values that include one or more attribute values measured at a cell pool screening stage of the candidate cell lines. The method also includes determining a ranking of the candidate cell lines according to a product quality attribute associated with hypothetical small-scale screening cultures. Determining the ranking includes predicting, for each of the candidate cell lines, a value of the product quality attribute by analyzing the first and second plurality of attribute values using a machine learning based regression estimator, and comparing the predicted values. The method also includes causing an indication of the ranking to be presented to a user via a user interface.

Abstract: A method for facilitating clone selection includes generating time-sequence images of a well containing a medium, including a first image and a later, second image. The method also includes detecting, by one or more processors analyzing the first image, one or more candidate objects depicted in the first image, and, for each of the candidate objects, determining whether the object is a single cell by analyzing an image of the object using a convolutional neural network. The method further includes detecting, by analyzing the second image with the processor(s), a cell colony depicted in the second image, and determining, by the processor(s), whether the colony was formed from only one cell based at least on whether each candidate object was determined to be a single cell. The method further includes generating, by the processor(s), output data indicating whether the colony was formed from only one cell.