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We propose a general framework for a collaborative machine learning system toassist bioscience researchers with the task of labeling specific cellidentities from microscopic still or video imaging. The distinguishing featuresof this approach versus prior approaches include: (1) use of a statisticalmodel of cell features that is iteratively improved, (2) generation ofprobabilistic guesses at cell ID rather than single best-guesses for each cell,(3) tracking of joint probabilities of features within and across cells, and(4) ability to exploit multi-modal features, such as cell position, morphology,reporter intensities, and activity. We provide an example implementation ofsuch a system applicable to labeling fluorescently tagged \textit{C. elegans}neurons. As a proof of concept, we use a generative spring-mass model tosimulate sequences of cell imaging datasets with variable cell positions andfluorescence intensities. Training on synthetic data, we find that atlases thattrack inter-cell positional correlations give higher labeling accuracies thanthose that treat cell positions independently. Tracking an additional featuretype, fluorescence intensity, boosts accuracy relative to a position-onlyatlas, suggesting that multiple cell features could be leveraged to improveautomated label predictions.

A probabilistic atlas for cell identification