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

Traub, Manuel; Stegmaier, Johannes


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.

Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Forschungsbericht/Preprint
Publikationsdatum 01.10.2020
Sprache Englisch
Identifikator KITopen-ID: 1000125643
HGF-Programm 47.01.02 (POF III, LK 01) Biol.Netzwerke u.Synth.Regulat. IAI
Umfang 10 S.
Nachgewiesen in arXiv
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