Automated Annotation of Cell Identities in Dense Cellular Images
Assigning cell identities in dense image stacks is critical for many applications, for comparing data across animals and experiment conditions, and investigating properties of specific cells. Conventional methods are laborious, require experience, and could introduce bias. We present a generalizable framework based on Conditional Random Fields models for automatic cell identification. This approach searches for optimal arrangements of labels that maximally preserves prior knowledge such as geometrical relationships. The algorithm shows better accuracy and more robust handling of perturbations, e.g. missing cells and position variability, with both synthetic and experimental ground-truth data. The framework is generalizable across strains, imaging conditions, and easily builds and utilizes active data-driven atlases, which further improves accuracy. We demonstrate the utility in gene-expression pattern analysis, multi-cellular calcium imaging, and whole-brain imaging experiments. Thus, our framework is highly valuable to a wide variety of annotation scenarios including in zebrafish, Drosophila, hydra, and mouse brains.
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