Quantitative studies of dynamic behaviors of live neurons are currently limited by the slowness, subjectivity, and tedium of manual analysis of changes in time-lapse image sequences. Challenges to automation include the complexity of the changes of interest,
the presence of obfuscating and uninteresting changes due to illumination variations and other imaging artifacts, and the sheer volume of recorded data. This paper describes a highly automated approach that not only detects the interesting changes selectively,
but also generates quantitative analyses at multiple levels of detail. Detailed quantitative neuronal morphometry is generated for each frame. Frame-to-frame neuronal changes are measured and labeled as growth, shrinkage, merging, or splitting, as would be done by a human expert. Finally, events unfolding over longer durations, such as apoptosis and axonal specification, are automatically inferred from the short-term changes. The proposed method is based on a Bayesian model selection crite