Automated ranking of stem cell colonies by translating biological rules to computational models

This paper addresses the problem of automating an image ranking process for stem cell colonies. We automate the manual process in a novel way: instead of fitting off-the-shelf image features and colony ranks to prediction models, we define a new feature set that uniquely characterizes the visual clues from images of the colonies and biological rules experts use to rank colonies from image data. Our automation considers several factors: the inconsistency of manually assigned stem cell colony ranks, the type of image segmentation to detect stem cell colonies (manual and automated), the type of image feature set (off-the-shelf vs. custom designed), and an underlying relationship between input colony features and output stem cell colony ranks (linear and nonlinear). The novelty of our work lies in automating stem cell colony ranking while preserving the connection between visually perceived quality characteristics of stem cell colonies, and image colony features combined with a computational prediction model. The main contribution of our work is in demonstrating the benefits of direct interpretation of biological rules to automation of stem cell colony ranking. We also outline a method for establishing relationships between the commonly used Haralick features and our custom-designed features.

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