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

Towards Automatic Embryo Staging in 3D+T Microscopy Images using Convolutional Neural Networks and PointNets