  We propose a general framework for a collaborative machine learning system to
assist bioscience researchers with the task of labeling specific cell
identities from microscopic still or video imaging. The distinguishing features
of this approach versus prior approaches include: (1) use of a statistical
model of cell features that is iteratively improved, (2) generation of
probabilistic 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 of
such a system applicable to labeling fluorescently tagged \textit{C. elegans}
neurons. As a proof of concept, we use a generative spring-mass model to
simulate sequences of cell imaging datasets with variable cell positions and
fluorescence intensities. Training on synthetic data, we find that atlases that
track inter-cell positional correlations give higher labeling accuracies than
those that treat cell positions independently. Tracking an additional feature
type, fluorescence intensity, boosts accuracy relative to a position-only
atlas, suggesting that multiple cell features could be leveraged to improve
automated label predictions.
