  Automatic analyses and comparisons of different stages of embryonic
development largely depend on a highly accurate spatiotemporal alignment of the
investigated data sets. In this contribution, we assess multiple approaches for
automatic staging of developing embryos that were imaged with time-resolved 3D
light-sheet microscopy. The methods comprise image-based convolutional neural
networks as well as an approach based on the PointNet architecture that
directly operates on 3D point clouds of detected cell nuclei centroids. The
experiments with four wild-type zebrafish embryos render both approaches
suitable for automatic staging with average deviations of 21 - 34 minutes.
Moreover, a proof-of-concept evaluation based on simulated 3D+t point cloud
data sets shows that average deviations of less than 7 minutes are possible.
